Publications

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Articles

Berger, Y., & Patilea, V. (2022). A semi-parametric empirical likelihood approach for conditional estimating equations under endogenous selection: empirical likelihood approach for conditional estimating equations. Econometrics and Statistics, 24, 151-163. https://doi.org/10.1016/j.ecosta.2021.12.004
Englezou, Y., Waite, T. W., & Woods, D. (2022). Approximate Laplace importance sampling for the estimation of expected Shannon information gain in high-dimensional Bayesian design for nonlinear models. Statistics and Computing, 32(5), [82]. https://doi.org/10.1007/s11222-022-10159-2
Parag, K. V., Pybus, O. G., & Wu, C-H. (2022). Are skyline plot-based demographic estimates overly dependent on smoothing prior assumptions? Systematic Biology, 71(1), 121-138. https://doi.org/10.1093/sysbio/syab037
Cao, L., Russo, D., Matthews, E. S., Lapkin, A., & Woods, D. (2022). Computer-aided design of formulated products: A bridge design of experiments for ingredient selection. Computers and Chemical Engineering, 169, [108083]. https://doi.org/10.1016/j.compchemeng.2022.108083
Dawber, J., Salvati, N., Fabrizi, E., & Tzavidis, N. (Accepted/In press). Expectile regression for multicategory outcomes with application to small area estimation of labour force participation. Journal of the Royal Statistical Society: Series A (Statistics in Society), 185(S2), S590-S619. https://doi.org/10.1111/rssa.12953
Harvey, C. M., Newell, M-L., & Padmadas, S. S. (2022). Maternal socioeconomic status and infant feeding practices underlying pathways to child stunting in Cambodia: structural path analysis using cross-sectional population data. BMJ Open, 12(11), [e055853]. https://doi.org/10.1136/bmjopen-2021-055853
Tackney, M. S., Woods, D., & Shpitser, I. (2022). Nonmyopic and pseudo-nonmyopic approaches to optimal sequential design in the presence of covariates. Journal of Statistical Computation and Simulation. https://doi.org/10.1080/00949655.2022.2113788
Maslovskaya, O., & Lugtig, P. (2022). Representativeness in six waves of CROss-National online survey (CRONOS) panel. Journal of the Royal Statistical Society. Series A (General), 185(3), 851-871. https://doi.org/10.1111/rssa.12801
Singh, A., Upadhyay, A. K., Kumar, K., Singh, A., Amoako Johnson, F., & Padmadas, S. S. (2022). Spatial heterogeneity in son preference across India’s 640 districts: An application of small-area estimation. Demographic Research, 47(26), 793–842. [26]. https://doi.org/10.4054/DemRes.2022.47.26
Little, P., Francis, N. A., Stuart, B., O'Reilly, G., Thompson, N., Becque, T., Hay, A. D., Wang, K., Sharland, M., Harnden, A., Yao, G., Raftery, J., Zhu, S., Little, J., Hookham, C., Rowley, K., Euden, J., Harman, K., Coenen, S., ... Verheij, T. (2021). Antibiotics for lower respiratory tract infection in children presenting in primary care in England (ARTIC PC): a double-blind, randomised, placebo-controlled trial. Lancet (London, England), 398(10309), 1417-1426. https://doi.org/10.1016/S0140-6736(21)01431-8
Berger, Y., Chiodini, P., & Zenga, M. (2021). Bounds for monetary-unit sampling in auditing: an adjusted empirical likelihood approach. Statistical Papers, 62(6), 2739-2761. https://doi.org/10.1007/s00362-020-01209-w
Faria, N. R., Mellan, T. A., Whittaker, C., Claro, I. M., Candido, D. D. S., Mishra, S., Crispim, M. A. E., Sales, F. C. S., Hawryluk, I., McCrone, J. T., Hulswit, R. J. G., Franco, L. A. M., Ramundo, M. S., De Jesus, J. G., Andrade, P. S., Coletti, T. M., Ferreira, G. M., Silva, C. A. M., Manuli, E. R., ... Sabino, E. C. (2021). Genomics and epidemiology of the P.1 SARS-CoV-2 lineage in Manaus, Brazil. Science, 372(6544), 815-821. [6544]. https://doi.org/10.1126/science.abh2644
Sirinukunwattana, K., Domingo, E., Richman, S. D., Redmond, K. L., Blake, A., Verrill, C., Leedham, S. J., Chatzipli, A., Hardy, C., Whalley, C. M., Wu, C. H., Beggs, A. D., McDermott, U., Dunne, P. D., Meade, A., Walker, S. M., Murray, G. I., Samuel, L., Seymour, M., ... Koelzer, V. H. (2021). Image-based consensus molecular subtype (imCMS) classification of colorectal cancer using deep learning. Gut, 70(3), 544-554. https://doi.org/10.1136/gutjnl-2019-319866
Berger, Y. (Accepted/In press). Testing conditional moment restriction models using empirical likelihood: empirical likelihood and conditional moment restriction. The Econometrics Journal. https://doi.org/10.1093/ectj/utab032
Wang, L., Zheng, C., Zhou, W., & Zhou, W-X. (Accepted/In press). A new principle for tuning-free Huber regression. Statistica Sinica. http://www3.stat.sinica.edu.tw/ss_newpaper/SS-2019-0045_na.pdf
Berger, Y. (2020). An empirical likelihood approach under cluster sampling with missing observations. Annals of the Institute of Statistical Mathematics, 72(1), 91-121. https://doi.org/10.1007/s10463-018-0681-x
Senarathne, S. G. J., Overstall, A., & McGree, J. (2020). Bayesian adaptive N-of-1 trials for estimating population and individual treatment effects. Statistics in Medicine, 39(29), 4499-4518.
Overstall, A., Woods, D., & Parker, B. (2020). Bayesian optimal design for ordinary differential equation models with application in biological science. Journal of the American Statistical Association, 115(530), 583-598. https://doi.org/10.1080/01621459.2019.1617154
Powell, B., & Smith, P. A. (2020). Computing expectations and marginal likelihoods for permutations. Computational Statistics, 35(2), 871-891. https://doi.org/10.1007/s00180-019-00901-2
Berger, Y., & Gedik Balay, İ. (2020). Confidence intervals of Gini coefficient under unequal probability sampling. Journal of Official Statistics, 36(2), 237-249. https://doi.org/10.2478/jos-2020-0013
Bohning, D., & Ogden, H. (2020). General flation models for count data. Metrika, 84(2), 245-261. https://doi.org/10.1007/s00184-020-00786-y
Waite, T., & Woods, D. (2020). Minimax-efficient random experimental design strategies with application to model-robust design for prediction. Journal of the American Statistical Association, 0, 1-14. https://doi.org/10.1080/01621459.2020.1863221
Oguz Alper, M., & Berger, Y. (2020). Modelling multilevel data under complex sampling designs: an empirical likelihood approach. Computational Statistics and Data Analysis, 145, 1-16. [106906]. https://doi.org/10.1016/j.csda.2019.106906
Far, S. S., King, R., Bird, S., Overstall, A., Worthington, H., & Jewell, N. (Accepted/In press). Multiple systems estimation for modern slavery: robustness of list omission and combination. Crime and Delinquency.
Cruyff, M., Overstall, A., Papathomas, M., & McCrea, R. (2020). Multiple systems estimation of victims of human trafficking: Model assessment and selection. Crime and Delinquency. https://doi.org/10.1177/0011128720981908
Smith, P. A., & Lorenc, B. (2020). Robust official business statistics methodology during COVID-19-related and other economic downturns. Statistical Journal of the International Association for Official Statistics, 37(4), 1079-1084. https://doi.org/10.3233/SJI-210870
Smith, P. A., & Yung, W. (2019). A review and evaluation of the use of longitudinal approaches in business surveys. Longitudinal and Life Course Studies, 10(4), 491-511. https://doi.org/10.1332/175795919X15694142999134
Haziza, D., & Smith, P. A. (2019). An interview with Chris Skinner. International Statistical Review, 87(3), 451-470. https://doi.org/10.1111/insr.12356
Overstall, A., & McGree, J. (2019). Bayesian design of experiments for intractable likelihood models using coupled auxiliary models and multivariate emulation. Bayesian Analysis, 15(1), 103-131. https://doi.org/10.1214/19-BA1144
Lee, K. M., Biedermann, S., & Mitra, R. (2019). D-optimal designs for multiarm trials with dropouts. Statistics in Medicine, 38(15), 2749-2766. https://doi.org/10.1002/sim.8148
Berger, Y., & Kabzinska, E. (2019). Empirical likelihood approach for aligning information from multiple surveys. International Statistical Review. https://doi.org/10.1111/insr.12337
Brendish, N., Mills, S., Ewings, S., & Clark, T. (2019). Impact of point-of-care testing for respiratory viruses on antibiotic use in adults with exacerbation of airways disease. Journal of Infection, 79(4), 357-362. https://doi.org/10.1016/j.jinf.2019.06.010
Zheng, C., Ferrari, D., & Yang, Y. (2019). Model selection confidence sets by likelihood ratio testing. Statistica Sinica, 29, 827-851. https://doi.org/10.5705/ss.202017.0006
Tollenaar, N., & Van Der Heijden, P. (2019). Optimizing predictive performance of criminal recidivism models using registration data with binary and survival outcomes. PLoS ONE, 14(3), 1-37. [e0213245]. https://doi.org/10.1371/journal.pone.0213245
Bavdaž, M., Giesen, D., Moore, D. L., Smith, P. A., & Jones, J. (2019). Qualitative testing for official establishment survey questionnaires. Survey Research Methods, 13(3), 267-288. https://doi.org/10.18148/srm/2019.v13i3.7366
Zheng, C., Ferrari, D., Zhang, M., & Baird, P. (2019). Ranking the importance of genetic factors by variable‐selection confidence sets. Journal of the Royal Statistical Society, Series C (Applied Statistics), 68(3), 727-749. https://doi.org/10.1111/rssc.12337
Smith, P. A. (2019). Shelterbelt trials in the Outer Hebrides after fifty years: a study in competition, survival and succession. Scottish Forestry, 72(2), 23-32.
Brown, J. J., Sexton, C., Abbott, O., & Smith, P. A. (2019). The framework for estimating coverage in the 2011 Census of England and Wales: combining dual-system estimation with ratio estimation. Statistical Journal of the International Association for Official Statistics, 35(3), 481-499. https://doi.org/10.3233/SJI-180426
Bohning, D., & Van Der Heijden, P. (2019). The identity of the zero-truncated, one-inflated likelihood and the zero-one-truncated likelihood for general count densities with an application to drink-driving in Britain. The Annals of Applied Statistics, 13(2), 1198-1211. https://doi.org/10.1214/18-AOAS1232
Stein, M. L., Buskens, V., Van Der Heijden, P. G. M., Van Steenbergen, J. E., Wong, A., Bootsma, M. C. J., Kretzschmar, M. E. E., & Lau, E. H. Y. (Ed.) (2018). A stochastic simulation model to study respondent-driven recruitment. PLoS ONE, 13(11), 1-19. [e0207507]. https://doi.org/10.1371/journal.pone.0207507
Overstall, A., McGree, J., & Drovandi, C. (2018). An approach for finding fully Bayesian optimal designs using normal-based approximations to loss functions. Statistics and Computing, 28(2), 343-358. https://doi.org/10.1007/s11222-017-9734-x
Van Der Heijden, P., Smith, P., Cruyff, M., & Bakker, B. (2018). An overview of population size estimation where linking registers results in incomplete covariates, with an application to mode of transport of serious road casualties. Journal of Official Statistics, 34(1), 239-263. https://doi.org/10.1515/jos-2018-0011
Galofré-Vilà, G., Guntupalli, A. M., Harris, B. J., & Hinde, P. (2018). Climate effects and stature since 1800. Social Science History, 42(4), 763-794. https://doi.org/10.1017/ssh.2018.27
Kienstra, N., & Van Der Heijden, P. (2018). Doing philosophy effectively II: a replication and elaboration of student learning in classroom teaching. PLoS ONE, 13(12), [e0208128]. https://doi.org/10.1371/journal.pone.0208128
Berger, Y. (2018). Empirical likelihood approaches in survey sampling. The Survey Statistician, (78), 22-31. http://isi-iass.org/home/wp-content/uploads/Survey_Statistician_July_2018.pdf
van Nes, Y., Bloemers, J., Kessels, R., Van Der Heijden, P. G. M., van Rooij, K., Gerritsen, J., deRogatis, L., & Tuiten, A. (2018). Psychometric properties of the Sexual Event Diary in a sample of Dutch women with Female Sexual Interest /Arousal Disorder. The Journal of Sexual Medicine, 15(5), 722-731. https://doi.org/10.1016/j.jsxm.2018.03.082
Heck, D., Overstall, A., Gronau, Q., & Wagenmakers, E-J. (2018). Quantifying uncertainty in transdimensional Markov chain Monte Carlo using discrete Markov models. Statistics and Computing, 29(4), 631-643. https://doi.org/10.1007/s11222-018-9828-0
Clark, R. G., Kokic, P., & Smith, P. A. (2017). A comparison of two robust estimation methods for business surveys. International Statistical Review, 85(2), 270-289. https://doi.org/10.1111/insr.12177
Smith, P. A., Lutz, M., Ziegler, R., & Piątek, M. (2017). Anther smuts of Silene acaulis and S. uniflora in the Outer Hebrides, including an assessment of ITS genotypes of Microbotryum silenes-acaulis. IMA Fungus, 8(1), 107-116. https://doi.org/10.5598/imafungus.2017.08.01.08
Woods, D. C., Overstall, A. M., Adamou, M., & Waite, T. W. (2017). Bayesian design of experiments for generalised linear models and dimensional analysis with industrial and scientific application. Quality Engineering, 29(1), 91-103. https://doi.org/10.1080/08982112.2016.1246045
Overstall, A., & Woods, D. (2017). Bayesian design of experiments using approximate coordinate exchange. Technometrics, 59, 458-470. https://doi.org/10.1080/00401706.2016.1251495
Smith, P. A., & James, G. J. (2017). Changing industrial classification to SIC(2007) at the UK Office for National Statistics. Journal of Official Statistics, 33(1), 223-247. https://doi.org/10.1515/jos-2017-0012
Zhou, S., Wan, F., Liu, W., & Bretz, F. (2017). Computation of an exact confidence set for a maximum point of a univariate polynomial function in a given interval. Statistics & Probability Letters, 122, 157-161. https://doi.org/10.1016/j.spl.2016.10.032
Adams, S., Jobson, M., Sangnawakij, P., Heetun, A., Thaventhiran, A., Joohal, N., Böhning, D., & Stanton, M. (2017). Does thoracoscopy have advantages over open surgery for asymptomatic congenital lung malformations? An analysis of 1626 resections. Journal of Pediatric Surgery, 52(2). https://doi.org/10.1016/j.jpedsurg.2016.11.014
Godwin, R. T., & Böhning, D. (2017). Estimation of the population size by using the one-inflated positive Poisson model. Journal of the Royal Statistical Society, Series C (Applied Statistics), 66(2), 425-448. https://doi.org/10.1111/rssc.12192
Gilbert, R., Lafferty, R., Hagger-Johnson, G., Harron, K., Zhang, L-C., Smith, P., Dibben, C., & Goldstein, H. (2017). GUILD: GUidance for Information about Linking Data sets. Journal of Public Health. https://doi.org/10.1093/pubmed/fdx037
Taylor, S., & Smith, P. (2017). Having a LAFF with Anthracoidea. Field Mycology, 18(1), 5-13. https://doi.org/10.1016/j.fldmyc.2017.01.004
Smith, P. (2017). Letter to the editors: a note on inference on variances from a variable measured on a Likert scale. Journal of the Royal Statistical Society. Series A: Statistics in Society, 180(3), 937-938. https://doi.org/10.1111/rssa.12277
Woods, D. C., McGree, J. M., & Lewis, S. M. (2017). Model selection via Bayesian information capacity designs for generalised linear models. Computational Statistics & Data Analysis, 113, 226-238. https://doi.org/10.1016/j.csda.2016.10.025
Shapiro, D., & Hinde, A. (2017). On the pace of fertility decline in sub-Saharan Africa. Demographic Research, 37, 1327-1338. https://doi.org/10.4054/DemRes.2017.37.40
Parker, B., Gilmour, S., & Schormans, J. (2017). Optimal design of experiments on connected units with application to social networks. Journal of the Royal Statistical Society: Series C (Applied Statistics), 1-24. https://doi.org/10.1111/rssc.12170
Little, P., Stuart, B., Hobbs, F. D. R., Kelly, J., Smith, E., Bradbury, K., Hughes, S., Smith, P., Moore, M., Lean, M. E. J., Margetts, B., Byrne, C., Griffin, S., Davoudianfar, M., Hooper, J., Yao, G., Zhu, S., Raftery, J., & Yardley, L. (2017). Randomised controlled trial and economic analysis of an internet-based weight management programme: POWeR+ (Positive Online Weight Reduction). Health Technology Assessment, 21(4), 1-61. https://doi.org/10.3310/hta21040
Kiatsupaibul, S., Hayter, A., & Liu, W. (2017). Rank constrained distribution and moment computations. Computational Statistics & Data Analysis, 105, 229-242. https://doi.org/10.1016/j.csda.2016.08.009
Hinde, A. (2017). Review: Peter Razzell, Mortality, marriage and population growth in England, 1550-1850. (London: Caliban Books, 2016. pp. 135. 53 tabs. ISBN 9780904573190 Pbk £10). The Economic History Review, 1-3.
Nason, G. P., Powell, B., Elliott, D., & Smith, P. (2017). Should we sample a time series more frequently? Decision support via multirate spectrum estimation. Journal of the Royal Statistical Society: Series A (Statistics in Society), 180(2), 353-407. https://doi.org/10.1111/rssa.12210
Luna, A., Zhang, L-C., Whitworth, A., & Piller, K. (2017). Small area estimates of the population distribution by ethnic group in England: a proposal using structure preserving estimators. Statistics in Transition, 16(4), 585-602. https://doi.org/10.21307/stattrans-2015-034
Smith, P. (2017). Some interesting salt-tolerant plants of roadsides in the Outer Hebrides. Hebridean Naturalist, 16, 15-23.
Sangnawakij, P., Böhning, D., Adams, S., Stanton, M., & Holling, H. (2017). Statistical methodology for estimating the mean difference in a meta-analysis without study-specific variance information. Statistics in Medicine, 36(9), 1395-1413. https://doi.org/10.1002/sim.7232
Cruyff, M., Van Dijk, J., & Van Der Heijden, P. (2017). The challenge of counting victims of human trafficking: Not on the record: A multiple systems estimation of the numbers of human trafficking victims in the Netherlands in 2010–2015 by year, age, gender, and type of exploitation. Chance, 30(3), 41-49. https://doi.org/10.1080/09332480.2017.1383113
van Es, Y., Bloemers, J., Van Der Heijden, P., Van Rooij, K., Gerritsen, J., Kessels, R., Derogatis, L., & Tuiten, A. (2017). The Sexual Event Diary (SED): development and validation of a standardized questionnaire for assessing female sexual functioning over discrete sexual events. The Journal of Sexual Medicine, 14(11), 1438-1450. https://doi.org/10.1016/j.jsxm.2017.09.008
Bass, M., & Sahu, S. (Accepted/In press). A comparison of centering parameterisations of Gaussian process based models for Bayesian computation using MCMC. Statistics and Computing, 1-29.
Böhning, D., Rocchetti, I., Alfo, M., & Holling, H. (2016). A flexible ratio regression approach for zero-truncated capture–recapture counts. Biometrics, 72(3), 697-706. https://doi.org/10.1111/biom.12485
Pelle, E., Hessen, D. J., & Van Der Heijden, P. G. M. (2016). A log-linear multidimensional Rasch model for capture-recapture. Statistics in Medicine, 35(4), 622-634. https://doi.org/10.1002/sim.6741
Utazi, C. E., Sahu, S. K., Atkinson, P. M., Tejedor Garavito, N., & Tatem, A. J. (2016). A probabilistic predictive Bayesian approach for determining the representativeness of health and demographic surveillance networks. Spatial Statistics, 17, 161-178. https://doi.org/10.1016/j.spasta.2016.05.006
Berger, Y. G., & Priam, R. (2016). A simple variance estimator of change for rotating repeated surveys: an application to the EU-SILC household surveys. Journal of the Royal Statistical Society: Series A (Statistics in Society), 179(1), 251-272. https://doi.org/10.1111/rssa.12116
Little, P., Stuart, B., Hobbs, F. D. R., Kelly, J., Smith, E. R., Bradbury, K. J., Hughes, S., Smith, P. W. F., Moore, M. V., Lean, M. E. J., Margetts, B. M., Byrne, C. D., Griffin, S., Davoudianfar, M., Hooper, J., Yao, G., Zhu, S., Raftery, J., & Yardley, L. (2016). An internet-based intervention with brief nurse support to manage obesity in primary care (POWeR+): a pragmatic, parallel-group, randomised controlled trial. The Lancet Diabetes & Endocrinology, 4(10), 821-828. https://doi.org/10.1016/S2213-8587(16)30099-7
Jaspers, S., Verbeke, G., Bohning, D., & Aerts, M. (2016). Application of the Vertex Exchange Method to estimate a semi-parametric mixture model for the MIC density of Escherichia coli isolates tested for susceptibility against ampicillin. Biostatistics, 17(1), 94-107. https://doi.org/10.1093/biostatistics/kxv030
Tabassum, F., Mohan, J., & Smith, P. (2016). Association of volunteering with mental well-being: a lifecourse analysis of a national population-based longitudinal study in the UK. BMJ Open, 6(8), e011327-e0113235. https://doi.org/10.1136/bmjopen-2016-011327, https://doi.org/10.1136/bmjopen-2016-011327
Bijak, J., & Bryant, J. (2016). Bayesian demography 250 years after Bayes. Population Studies, 70(1), 1-19. https://doi.org/10.1080/00324728.2015.1122826
Liu, W., Han, Y., Bretz, F., Wan, F., & Yang, P. (2016). Counting by weighing: know your numbers with confidence. Journal of the Royal Statistical Society. Series C: Applied Statistics, 65(4), 641-648. https://doi.org/10.1111/rssc.12142
Berger, Y. G., & De La Riva Torres, O. (2016). Empirical likelihood confidence intervals for complex sampling designs. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 78(2), 319-341. https://doi.org/10.1111/rssb.12115
Berger, Y. G. (2016). Empirical likelihood inference for the Rao-Hartley-Cochran sampling design. Scandinavian Journal of Statistics, 43(3), 721-735. https://doi.org/10.1111/sjos.12200
Bowman, V., & Woods, D. (2016). Emulation of multivariate simulators using thin-plate splines with application to atmospheric dispersion. Journal of Uncertainty Quantification, 4(1), 1323-1344. https://doi.org/10.1137/140970148
Özen, D., Böhning, D., & Gürcan, İ. S. (2016). Estimation of stray dog and cat populations in metropolitan Ankara, Turkey. Turkish Journal of Veterinary and Animal Sciences, 40(1), 7-12. https://doi.org/10.3906/vet-1505-70
Clegg, R., Parker, B., & Rio, M. (2016). Likelihood-based assessment of dynamic networks. Journal of Complex Networks, 1-18. https://doi.org/10.1093/comnet/cnv031
Van Der Heijden, P. G. M., & van Buuren, S. (2016). Looking back at the Gifi system of nonlinear multivariate analysis. Journal of Statistical Software, 73(4), 1-8. https://doi.org/10.18637/jss.v073.i04
Vieira, M. D. T., Smith, P., & Salgueiro, M. D. F. (2016). Misspecification effects in the analysis of panel data. Journal of Official Statistics, 32(2), 487-505. https://doi.org/10.1515/jos-2016-0025
Oguz Alper, M., & Berger, Y. G. (2016). Modelling complex survey data with population level information: an empirical likelihood approach. Biometrika, 103(2), 447-459. https://doi.org/10.1093/biomet/asw014
Pirani, M., Panton, A., Purdie, D., & Sahu, S. (2016). Modelling macronutrient dynamics in the Hampshire Avon river: a Bayesian approach to estimate seasonal variability and total flux. Science of the Total Environment, 572, 1449-1460. https://doi.org/10.1016/j.scitotenv.2016.04.129
Peng, J., Liu, W., Bretz, F., & Shked, Z. (2016). Multiple confidence intervals for selected parameters adjusted for the false coverage rate in monotone dose–response microarray experiments. Biometrical Journal, 1-14. https://doi.org/10.1002/bimj.201500254
Lee, M., & Mitra, R. (2016). Multiply imputing missing values in data sets with mixed measurement scales using a sequence of generalised linear models. Computational Statistics & Data Analysis, 95, 24-38. https://doi.org/10.1016/j.csda.2015.08.004
Overstall, A., & Woods, D. (2016). Multivariate emulation of computer simulators: model selection and diagnostics with application to a humanitarian relief model. Journal of the Royal Statistical Society. Series C: Applied Statistics, 65(4), 483-505. https://doi.org/10.1111/rssc.12141
Anan, O., Bohning, D., & Maruotti, A. (2016). Population size estimation and heterogeneity in capture–recapture data: a linear regression estimator based on the Conway–Maxwell–Poisson distribution. Statistical Methods & Applications, 1-31. https://doi.org/10.1007/s10260-016-0358-7
Lin, S. X., Morrison, L., Smith, P., Hargood, C., Weal, M., & Yardley, L. (2016). Properties of bootstrap tests for N-of-1 studies. British Journal of Mathematical and Statistical Psychology, 1-29. https://doi.org/10.1111/bmsp.12071
Böhning, D. (2016). Ratio plot and ratio regression with applications to social and medical sciences. Statistical Science, 31(2), 205-218. https://doi.org/10.1214/16-STS548
Han, Y., Liu, W., Bretz, F., Wan, F., & Yang, P. (2016). Statistical calibration and exact one-sided simultaneous tolerance intervals for polynomial regression. Journal of Statistical Planning and Inference, 168, 90-96. https://doi.org/10.1016/j.jspi.2015.07.005
Tollenaar, N., Wartna, B. S. J., Van Der Heijden, P., & Bogaerts, S. (2016). StatRec - performance, validation and preservability of a static risk prediction instrument. Bulletin de Méthodologie Sociologique, 129(1), 25-44. https://doi.org/10.1177/0759106315615504
De Luca, F., & Hinde, A. (2016). The effectiveness of the 'Back to Sleep' campaigns among healthcare professionals in the past 20 years: a systematic review. BMJ Open, 6, 1-18. https://doi.org/10.1136/bmjopen-2016-011435
Berger, Y. G., & Emilio L., E. (2016). Variance estimation of imputed estimators of change for repeated rotating surveys. International Statistical Review. https://doi.org/10.1111/insr.12197
Li, D., Linton, O., & Lu, Z. (2015). A flexible semiparametric forecasting model for time series. Journal of Econometrics, 187(1), 345-357. https://doi.org/10.1016/j.jeconom.2015.02.025
Bohning, D., & Viwatwongkasem, C. (2015). A miscellaneous note on the equivalence of two poisson likelihoods. Journal of Biometrics & Biostatistics, 6(2), 1-3. https://doi.org/10.4172/2155-6180.1000231
Pirani, M., Best, N., Blangiardo, M., Liverani, S., Atkinson, W. R., & Fuller, W. G. (2015). Analysing the health effects of simultaneous exposure to physical and chemical properties of airborne particles. Environment International, 79, 56-64. https://doi.org/10.1016/j.envint.2015.02.010
Wisniowski, A., Smith, P. W. F., Bijak, J., Raymer, J., & Forster, J. J. (2015). Bayesian population forecasting: extending the Lee-Carter Method. Demography, 52(3), 1035-1059. https://doi.org/10.1007/s13524-015-0389-y
Bohning, D., & van der Heijden, P. (2015). Correspondence: some general points regarding Ledberg and Wennberg, BMC Medical Research Methodology 2014 April 27;14:58. BMC Medical Research Methodology, 15(51), 15. https://doi.org/10.1186/s12874-015-0043-9
Waite, T. W., & Woods, D. C. (2015). Designs for generalized linear models with random block effects via information matrix approximations. Biometrika, 102(3), 677-693. https://doi.org/10.1093/biomet/asv005
van der Ham, I. J. M., Baalbergen, H., van der Heijden, P., Postma, A., Braspenning, M., & van der Kuil, M. N. A. (2015). Distance comparisons in virtual reality: effects of path, context, and age. Frontiers in Psychology, 6, 1-7. [1103]. https://doi.org/10.3389/fpsyg.2015.01103
Kienstra, N. H. H., Imants, J. E., Kraskens, M., & van der Heijden, P. (2015). Doing philosophy effectively: student learning in classroom teaching. PLoS ONE, 10(9), 1-23. https://doi.org/10.1371/journal.pone.0137590
Stein, M. L., van Steenbergen, J. E., Buskens, V., van der Heijden, P., Koppeschaar, C. E., Bengtsson, L., Thorson, A. E., & Kretzschmar, M. E. E. (2015). Enhancing syndromic surveillance with on-line respondent driven detection. American Journal of Public Health, 105(8), e90-e97. https://doi.org/10.2105/AJPH.2015.302717
Coumans, A. M., Cruyff, M. J. L. F., Van der Heijden, P. G. M., Wolf, J., & Schmeets, H. (2015). Estimating homelessness in the Netherlands using a capture-recapture approach. Social Indicators Research, 130(1), 189-212. https://doi.org/10.1007/s11205-015-1171-7
Priam, R., Nadif, M., & Govaert, G. (2015). Generalized topographic block model. Neurocomputing, 1-8. https://doi.org/10.1016/j.neucom.2015.04.115
Parker, B. M., Schormans, J., & Gilmour, S. G. (2015). Increasing throughput in IEEE 802.11 by optimal selection of backoff parameters. IET Networks, 4(1), 21 - 29. https://doi.org/10.1049/iet-net.2013.0021
Vieira, M. D. T., Salgueiro, M. D. F., & Smith, P. W. F. (2015). Investigating impacts of complex sampling on latent growth curve modelling. Journal of Applied Statistics, 1-12. https://doi.org/10.1080/02664763.2015.1100590
Staetsky, L. D., & Hinde, P. (2015). Jewish mortality reconsidered. Journal of Biosocial Science, 47(3), 376-401. https://doi.org/10.1017/S0021932014000133
Hallin, M., Lu, Z., Paindaveine, D., & Šiman, M. (2015). Local bilinear multiple-output quantile/depth regression. Bernoulli, 21(3), 1435-1466. https://doi.org/10.3150/14-BEJ610
Yildiz, D., & Smith, P. W. F. (2015). Models for combining aggregate-level administrative data in the absence of a traditional census. Journal of Official Statistics, 31(3), 431-451. https://doi.org/10.1515/jos-2015-0026
Morandi, G., Periche Tomas, E., & Pirani, M. (2015). Mortality risk in alcoholic patients in northern Italy: comorbidity and treatment retention effects in a 30-Year follow-up study. Alcohol and Alcoholism. https://doi.org/10.1093/alcalc/agv058
Berger, Y. G., & Francisco Munoz, J. (2015). On estimating quantiles using auxiliary information. Journal of Official Statistics, 31(1), 101-119. https://doi.org/10.1515/JOS-2015-0005
Parker, B., Gilmour, S., Schormans, J., & Maruri-Aguilar, H. (2015). Optimal design of measurements on queueing systems. Queueing Systems, 79(3), 365-390. https://doi.org/10.1007/s11134-014-9421-y
Konstantinou, M., Biedermann, S., & Kimber, A. C. (2015). Optimal designs for full and partial likelihood information - with application to survival models. Journal of Statistical Planning and Inference, 165, 27–37. https://doi.org/10.1016/j.jspi.2015.03.007
Bijak, J., Alberts, I., Alho, J., Bryant, J., Buettner, T., Falkingham, J. C., Forster, J. J., Gerland, P., Keilman, N., King, T., O'Hagan, A., Onorante, L., Owens, D., Raftery, A. E., Ševčíková, H., & Smith, P. W. F. (2015). Probabilistic population forecasts for informed decision making. Letter to the Editor. Journal of Official Statistics, 31(4), 537-544. https://doi.org/10.1515/JOS-2015-0033
Ankenman, B. E., Cheng, R. C. H., & Lewis, S. M. (2015). Screening for dispersion effects by sequential bifurcation. ACM Transactions on Modeling and Computer Simulation, 25(1). https://doi.org/10.1145/2651364
Gerritse, S., van der Heijden, P. G. M., & Bakker, B. F. M. (2015). Sensitivity of population size estimation for violating parametric assumptions in log linear models. Journal of Official Statistics, 31(3). https://doi.org/10.1515/jos-2015-0022
Hinde, A. (2015). Sex differentials in phthisis mortality in England and Wales, 1861-1870. The History of the Family, 20(3), 366-390. https://doi.org/10.1080/1081602X.2015.1051077
Cruyff, M., Bockenholt, U., & van der Heijden, P. G. M. (2015). The multidimensional randomized response design: estimating different aspects of the same sensitive behavior. Behavior Research Methods, 1-10. https://doi.org/10.3758/s13428-015-0583-2
Lecomte, C. (2015). TRAX: An approach for the time rational analysis of complex dynamic systems. International Journal for Numerical Methods in Engineering, 102(7), 1371-1399. https://doi.org/10.1002/nme.4841
Kienstra, N. H. H., & van der Heijden, P. (2015). Using correspondence analysis in multiple case studies. Bulletin de Méthodologie Sociologique, 128(1), 5-22. https://doi.org/10.1177/0759106315596920
Berger, Y., & Oguz Alper, M. (2015). Variance estimation of change in poverty rates: an application to the Turkish EU-SILC Survey. Journal of Official Statistics, 31(2), 155-175. https://doi.org/10.1515/JOS-2015-0012
Oguz-Alper, M., & Berger, Y. G. (Accepted/In press). Variance estimation of change of poverty based upon the Turkish EU-SILC survey. Journal of Official Statistics, 1-30.
Lee, D., Rushworth, A., & Sahu, S. K. (2014). A Bayesian localised conditional auto-regressive model for estimating the health effects of air pollution. Biometrics, 70(2), 419-429. https://doi.org/10.1111/biom.12156
Sambo, F., Borrotti, M., & Mylona, K. (2014). A coordinate-exchange two-phase local search algorithm for the D- and I-optimal design of split-plot experiments. Computational Statistics and Data Analysis, 71, 1193-1207. https://doi.org/10.1016/j.csda.2013.03.015
Lecomte, C. (2014). A frequency averaging framework for the solution of complex dynamic systems. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 470(2166), 1-28. https://doi.org/10.1098/rspa.2013.0743
Parpoula, C., Drosou, K., Koukouvinos, C., & Mylona, K. (2014). A new variable selection approach inspired by supersaturated designs given a large-dimensional dataset. Journal of Data Science, 12, 35-52.
Sun, Y., Yan, H., Zhang, W., & Lu, Z. (2014). A semiparametric spatial dynamic model. The Annals of Statistics, 42(2), 700-727. https://doi.org/10.1214/13-AOS1201
Parker, B. M., Schormans, J., & Gilmour, S. G. (2014). A utility based framework for optimal network measurement. IET Networks. https://doi.org/10.1049/iet-net.2013.0014
Li, Y. (Ed.), Jiang, Z., Du, C., Jablensky, A., Liang, H., Lu, Z., Ma, Y., & Teo, K. L. (2014). Analysis of schizophrenia data using a nonlinear threshold index logistic model. PLoS ONE, 9(10), e109454-[11pp]. https://doi.org/10.1371/journal.pone.0109454
Pirani, M., Gulliver, J., Fuller, G. W., & Blangiardo, M. (2014). Bayesian spatiotemporal modelling for the assessment of short-term exposure to particle pollution in urban areas. Journal of Exposure Science and Environmental Epidemiology, 24(3), 319-327. https://doi.org/10.1038/jes.2013.85
Capraro, V., Smyth, C., Mylona, K., & Niblo, G. A. (2014). Benevolent characteristics promote cooperative behaviour among humans. PLoS ONE, 9(8), 1-6. https://doi.org/10.1371/journal.pone.0102881
Stein, M. L., van Steenbergen, J. E., Buskens, V., van der Heijden, P. G. M., Chanyasanha, C., Tipayamongkholgul, M., Thorson, A. E., Bengtsson, L., Lu, X., & Kretzschmar, M. E. E. (2014). Comparison of contact patterns relevant for transmission of respiratory pathogens in Thailand and the Netherlands using respondent-driven sampling. PLoS ONE, 9(11), 1-21. https://doi.org/10.1371/journal.pone.0113711
Durrant, G. B., & D'Arrigo, J. (2014). Doorstep interactions and interviewer effects on the process leading to cooperation or refusal. Sociological Methods and Research, 1-29. https://doi.org/10.1177/0049124114521148
Tollenaar, N., van der Laan, A. M., & van der Heijden, P. G. M. (2014). Effectiveness of a prolonged incarceration and rehabilitation measure for high-frequency offenders. Journal of Experimental Criminology, 10(1), 29-58. https://doi.org/10.1007/s11292-013-9179-y
Vassallo, R., Durrant, G. B., Smith, P. W. F., & Goldstein, H. (2014). Interviewer effects on non-response propensity in longitudinal surveys: a multilevel modeling approach. Journal of the Royal Statistical Society: Series A (Statistics in Society), 1-17. https://doi.org/10.1111/rssa.12049
Charoensawat, S., Bohning, W., Böhning, D., & Holling, H. (2014). Meta-analysis and meta-modelling for diagnostic problems. BMC Medical Research Methodology, 14(56), 1-13. https://doi.org/10.1186/1471-2288-14-56
Stein, M. L., van Steenbergen, J. E., Chanyasanha, C., Tipayamongkholgul, M., Buskens, V., van der Heijden, P. G. M., Sabaiwan, W., Bengtsson, L., Lu, X., Thorson, A. E., & Kretzschmar, M. E. E. (2014). Online respondent-driven sampling for studying contact patterns relevant for the spread of close-contact pathogens: a pilot study in Thailand. PLoS ONE, 9(1), 1-11. [e85256]. https://doi.org/10.1371/journal.pone.0085256
van de Ven, P. M., & Woods, D. C. (2014). Optimal blocked minimum-support designs for non-linear models. Journal of Statistical Planning and Inference, 144, 152-159. https://doi.org/10.1016/j.jspi.2013.02.001
Mylona, K., Goos, P., & Jones, B. (2014). Optimal design of blocked and split-plot experiments for fixed effects and variance component estimation. Technometrics, 56(2), 132-144. https://doi.org/10.1080/00401706.2013.818579
Konstantinou, M., Biedermann, S., & Kimber, A. (2014). Optimal designs for two-parameter nonlinear models with application to survival models. Statistica Sinica, 24(1), 415-428. https://doi.org/10.5705/ss.2011.271
Fabrizi, E., Salvati, N., Pratesi, M., & Tzavidis, N. (2014). Outlier robust model-assisted small area estimation. Biometrical Journal, 56(1), 157-175. https://doi.org/10.1002/bimj.201200095
Smith, P., & Phipps, P. (2014). Preface. Introduction to the special issue on establishment surveys. Journal of Official Statistics, 30(4), 575-577. https://doi.org/10.2478/JOS-2014-0038
Tzavidis, N., Ranalli, M. G., Salvati, N., Dreassi, E., & Chambers, R. (2014). Robust small area prediction for counts. Statistical Methods in Medical Research. https://doi.org/10.1177/0962280214520731
Draguljic, D., Woods, D. C., Dean, A. M., Lewis, S. M., & Vine, A-J. E. (2014). Screening strategies in the presence of interactions (with discussion). Technometrics, 56(1), 1-28. https://doi.org/10.1080/00401706.2013.775900
Cruyff, M. J., & van der Heijden, P. G. M. (2014). Sensitivity analysis and calibration of population size estimates obtained with the zero-truncated Poisson regression model. Statistical Modelling, 14(5), 361–373. https://doi.org/10.1177/1471082X13511168
Bunte, T. L., Schoemaker, K., Hessen, D. J., van der Heijden, P., & Matthijs, W. (2014). Stability and change of ODD, CD and ADHD diagnosis in referred preschool children. Journal of Abnormal Child Psychology, 42(7), 1213-1224. https://doi.org/10.1007/s10802-014-9869-6
Bakker, A., van der Heijden, P. G. M., van Son, M. J. M., van de Schoot, R., Vandermeulen, E., Helsen, A., & Van Loey, N. E. E. (2014). The relationship between behavioural problems in preschool children and parental distress after a paediatric burn event. European Child & Adolescent Psychiatry, 23(9), 813-822. https://doi.org/10.1007/s00787-014-0518-y
Koukouvinos, C., Mylona, K., & Parpoula, C. (2013). A combination of variable selection and data mining techniques for high-dimensional statistical modelling. International Journal of Information and Decision Sciences, 5(2), 154-168. https://doi.org/10.1504/IJIDS.2013.053799
Belloc, F., Bernardi, M., Maruotti, A., & Petrella, L. (Accepted/In press). A dynamic hurdle model for zero-inflated panel traffic violation count data. Applied Economics Letters, 20(9), 837-841. https://doi.org/10.1080/13504851.2012.750447
Koukouvinos, C., Mylona, K., Skountzou, A., & Goos, P. (2013). A general construction method for five-level second-order rotatable designs. Communications in Statistics - Simulation and Computation. https://doi.org/10.1080/03610918.2012.687062
Böhning, D., Vidal-Diez, A., Lerdsuwansri, R., Vivatwongkasem, C., & Arnold, M. (2013). A generalization of Chao's estimator for covariate information. Biometrics, 69(4), 1033-1042. https://doi.org/10.1111/biom.12082
Sahu, S. K., Baffour, B., Minty, J., Harper, P., & Sarran, C. (2013). A hierarchical Bayesian model for improving short-term forecasting of hospital demand by including meteorological information. Journal of the Royal Statistical Society: Series A (Statistics in Society), 176, n/a. https://doi.org/10.1111/rssa.12008
Escobar, E. L., & Berger, Y. G. (2013). A jackknife variance estimator for self-weighted two-stage samples. Statistica Sinica, 23(2), 1-19. https://doi.org/10.5705/ss.2011.263
Molleman, T., & van der Heijden, P. G. M. (2013). A method to deal with dissimilar circumstances of public organizations in performance comparisons: evidence from Dutch prisons. Public Administration Research, 2(2). https://doi.org/10.5539/par.v2n2p1
Escobar, E. L., & Berger, Y. G. (2013). A new replicate variance estimator for unequal probability sampling without replacement. Canadian Journal of Statistics, 41(3), 508-524. https://doi.org/10.1002/cjs.11187
Böhning, D., Karasek, S., Terschüren, C., Annuß, R., & Fehr, R. (2013). A nonparametric random coefficient approach for life expectancy growth using a hierarchical mixture likelihood model with application to regional data from North Rhine-Westphalia (Germany). BMC Medical Research Methodology, 13(26), 1-13. https://doi.org/10.1186/1471-2288-13-36
Overstall, A., & Woods, D. C. (2013). A strategy for Bayesian inference for computationally expensive models with application to the estimation of stem cell properties. Biometrics, 69(2), 458-468. https://doi.org/10.1111/biom.12017
Durrant, G. B., D'Arrigo, J., & Steele, F. (2013). Analysing interviewer call record data by using a multilevel discrete-time event history modelling approach. Journal of the Royal Statistical Society: Series A (Statistics in Society), 176(1), 251-269.
Mark, T., Lemon, K., Vandenbosch, M., Bulla, J., & Maruotti, A. (2013). Capturing the evolution of customer–firm relationships: how customers become more (or less) valuable over time. Journal of Retailing. https://doi.org/10.1016/j.jretai.2013.04.001
Magrì, D., Corrà, U., Di Lenarda, A., Cattadori, G., Maruotti, A., Iorio, A., Mezzani, A., Giannuzzi, P., Mantegazza, V., Gondoni, E., Sinagra, G., Piepoli, M., Fiorentini, C., & Agostoni, P. (2013). Cardiovascular mortality and chronotropic incompetence in systolic heart failure: the importance of a reappraisal of current cut-off criteria. European Journal of Heart Failure. https://doi.org/10.1002/ejhf.36
Bunte, T. L., Laschen, S., Schoemaker, K., Hessen, D. J., van der Heijden, P. G. M., & Matthys, W. (2013). Clinical usefulness of observational assessment in the diagnosis of DBD and ADHD in preschoolers. Journal of Clinical Child & Adolescent Psychology, 42(6), 749-761. https://doi.org/10.1080/15374416.2013.773516
Bunte, T. L., Schoemaker, K., Hessen, D. J., van der Heijden, P. G. M., & Matthys, W. (2013). Clinical usefulness of the kiddie-disruptive behavior disorder schedule in the diagnosis of DBD and ADHD in preschool children. Journal of Abnormal Child Psychology, 41(5), 681-690. https://doi.org/10.1007/s10802-013-9732-1
Bakker, A., van der Heijden, P. G. M., Van Son, M. J. M., & Van Loey, N. E. E. (2013). Course of traumatic stress reactions in couples after a burn event to their young child. Health Psychology, 32(10), 1076-1083. https://doi.org/10.1037/a0033983
Durrant, G. B., & Kreuter, F. (2013). Editorial: The use of paradata in social survey research. Journal of the Royal Statistical Society: Series A (Statistics in Society), 176(1), 1-3. https://doi.org/10.1111/j.1467-985X.2012.01082.x
Sinibaldi, J., Durrant, G. B., & Kreuter, F. (2013). Evaluating the measurement error of interviewer observed paradata. Public Opinion Quarterly, 77(S1), 173-193.
Liu, W., Ah-Kine, P., Bretz, F., & Hayter, A. J. (2013). Exact simultaneous confidence intervals for a finite set of contrasts of three, four or five generally correlated normal means. Computational Statistics & Data Analysis, 57(1), 141-148. https://doi.org/10.1016/j.csda.2012.06.007
Abel, G. J., Bijak, J., Findlay, A. M., McCollum, D., & Wiśniowski, A. (2013). Forecasting environmental migration to the United Kingdom: an exploration using Bayesian models. Population and Environment, 35(2), 183-203. https://doi.org/10.1007/s11111-013-0186-8
Raymer, J., Wiśniowski, A., Forster, J. J., Smith, P. W. F., & Bijak, J. (2013). Integrated modeling of European migration. Journal of the American Statistical Association, 108(503), 801-819. https://doi.org/10.1080/01621459.2013.789435
Paredes Alpaca, R. I., Forastiere, F., & Pirani, M. (2013). Low exposure to lead and reproductive health: a cohort study of female workers in the ceramic industry of Emilia-Romagna (Northern Italy). Epidemiologia e prevenzione, 37(6), 367-375.
van der Heijden, P. G. M., Cruts, G., & Cruyff, M. (2013). Methods for population size estimation of problem drug users using a single registration. International Journal of Drug Policy, 24(6), 614-618. https://doi.org/10.1016/j.drugpo.2013.04.002
Pfeffermann, D. (2013). New important developments in small area estimation. Statistical Science, 28(1), 40-68. https://doi.org/10.1214/12-STS395
Maruotti, A., & Raponi, V. (2013). On baseline conditions for zero-inflated longitudinal count data. Communications in Statistics - Simulation and Computation. https://doi.org/10.1080/03610918.2012.714032
Fisher, V. A., Woods, D. C., & Lewis, S. M. (2013). Optimal design for prediction using local linear regression and the DSI-criterion. Statistics and Applications, 11(1 & 2), 33-54.
Stubbe, J. H., Chorus, A. M. J., Frank, L. E., de Hon, O., & van der Heijden, P. G. M. (2013). Prevalence of use of performance enhancing drugs by fitness centre members. Drug Testing and Analysis, 6(5), 434-438. https://doi.org/10.1002/dta.1525
Maruotti, A. (2013). Robust fitting of hidden Markov regression models under a longitudinal setting. Journal of Statistical Computation and Simulation. https://doi.org/10.1080/00949655.2013.763943
Osier, G., Berger, Y. G., & Goedeme, T. (Accepted/In press). Standard error estimation for the EU-SILC indicators of poverty and social exclusion. Eurostat Methodologies and Working Papers Series.
Holling, H., Böhning, W., Böhning, D., & Formann, A. K. (2013). The covariate-adjusted frequency plot. Statistical Methods in Medical Research, 1-15. https://doi.org/10.1177/0962280212473386
Martinez-Zarzoso, I., & Maruotti, A. (2013). The environmental Kuznets curve: functional form, time-varying heterogeneity and outliers in a panel setting. Environmetrics, 24(461-475). https://doi.org/10.1002/env.2232
Veiga, A., Smith, P. W. F., & Brown, J. J. (2013). The use of sample weights in multivariate multilevel models with an application to income data collected by using a rotating panel survey. Journal of the Royal Statistical Society. Series C: Applied Statistics, n/a, n/a. https://doi.org/10.1111/rssc.12020
Mylona, K., Macharia, H., & Goos, P. (2013). Three-level equivalent-estimation split-plot designs based on subset and supplementary difference set designs. IIE Transactions, 45(11), 1153-1165. https://doi.org/10.1080/0740817X.2012.723841
Boehning, D., Baksh, M. F., Lerdsuwnasri, R., & Gallagher, J. (2013). Use of the ratio plot in capture-recapture estimation. Journal of Computational and Graphical Statistics, 22(1), 135-155. https://doi.org/10.1080/10618600.2011.647174
Sparabombe, S., Guerra, F., Maruotti, A., De Sanctis, R., Di Giorgio, G., Palaia, G., Romeo, U., & Barbato, E. (2013). Validity and reliability of a new instrument for the evaluation of dental collaboration in disabled people. Epidemiology Biostatistics and Public Health, 10(4). https://doi.org/10.2427/13
Bowman, V. E., & Woods, D. C. (2013). Weighted space-filling designs. Journal of Simulation, 7(4), 249-263. https://doi.org/10.1057/jos.2013.8
Mitra, R., & Reiter, J. P. (2012). A comparison of two methods of estimating propensity scores after multiple imputation. Statistical Methods in Medical Research, 1-17. https://doi.org/10.1177/0962280212445945
Kimber, A., & Sarker, S-J. (2012). A covariance-based test for shared frailty in multivariate lifetime data. Journal of Applied Statistics, 38(11), 2509-2522. https://doi.org/10.1080/02664763.2012.720966
Ekholm, A., Jokinen, J., McDonald, J. W., & Smith, P. W. F. (2012). A latent class model for bivariate binary responses from twins. Journal of the Royal Statistical Society. Series C: Applied Statistics, 61(3), 493-514. https://doi.org/10.1111/j.1467-9876.2011.01034.x
Maruotti, A., & Rocci, R. (2012). A mixed non-homogeneous hidden Markov model for categorical data, with application to alcohol consumption. Statistics in Medicine, 31(9), 871-886. https://doi.org/10.1002/sim.4478
Zhang, L-C., & Fosen, J. (2012). A modeling approach for uncertainty assessment of register-based small area statistics. Journal of the Indian Society of Agricultural Statistics, 66, 91-104.
Bulla, J., Lagona, F., Maruotti, A., & Picone, M. (2012). A Multivariate Hidden Markov Model for the Identification of Sea Regimes from Incomplete Skewed and Circular Time Series. Journal of Agricultural, Biological and Environmental Statistics. https://doi.org/10.1007/s13253-012-0110-1
Perri, P. F., & van der Heijden, P. G. M. (2012). A property of the CHAID partitioning method for dichotomous randomized response data and categorical predictors. Journal of Classification, 29(1), 76-90. https://doi.org/10.1007/s00357-011-9094-8
Bakker, A., van Loey, N. E. E., van der Heijden, P. G. M., & van Son, M. J. M. (2012). Acute stress reactions in couples after a burn event to their young child. Journal of Pediatric Psychology, 37(10), 1127-1135. https://doi.org/10.1093/jpepsy/jss083
Piccirillo, G., Magri, D., Pappada', M., Maruotti, A., Ogawa, M., Han, S., Joung, B., Rossi, P., Nguyen, B. L., Lin, S-F., & Chen, P-S. (2012). Autonomic nerve activity and the short term variability of Tpeak–Tend interval in dogs with pacing-induced heart failure. Heart Rhythm.
Liu, Q., Dean, A., & Allenby, G. (2012). Bayesian designs for hierarchical linear models. Statistica Sinica, 22(1), 393-417. https://doi.org/10.5705/ss.2009.319
McCrea, R. L., Berger, Y. G., & King, M. B. (2012). Body mass index and common mental disorders: exploring the shape of the association and its moderation by age, gender and education. International Journal of Obesity, 36(3), 414-421. https://doi.org/10.1038/ijo.2011.65
Niwitpong, S., Böhning, D., van der Heijden, P. G. M., & Holling, H. (2012). Capture-recapture estimation based upon the geometric distribution allowing for heterogeneity. Metrika, 76(4), 495-519. https://doi.org/10.1007/s00184-012-0401-0
van Dijke, A., van der Hart, O., van Son, M., Bühring, M., van der Heijden, P., & Ford, J. D. (2012). Cognitive and affective dimensions of difficulties in emotional functioning in somatoform disorders and borderline personality disorder. Psychopathology, 46(3), 153-162. https://doi.org/10.1159/000338832
Magrì, D., Agostoni, P., Cauti, F. M., Musumeci, B., Assenza, G. E., De Cecco, C. N., Muscogiuri, G., Maruotti, A., Ricotta, A., Pagannone, E., Marino, L., Santini, D., Proietti, G., Serdoz, A., Paneni, F., Volpe, M., & Autore, C. (2012). Determinants of peak oxygen uptake in patients with hypertrophic cardiomyopathy: a single-center study. Internal and Emergency Medicine. https://doi.org/10.1007/s11739-012-0866-x
Raymer, J., Abel, G. J., & Rogers, A. (2012). Does specification matter? Experiments with simple multiregional probabilistic population projections. Environment and Planning A, 44(11), 2664-2686. https://doi.org/10.1068/a4533

Books

Chater, A. O., Woods, R. G., Stringer, R. N., Evans, D. A., & Smith, P. A. (2020). Downy mildews (Peronosporaceae) and White blister-rusts (Albuginaceae) of Wales. A.O. Chater.
Zhang, L-C., & Chambers, R. L. (Eds.) (2019). Analysis of integrated data. (1 ed.) (Statistics in Social and Behavioral Sciences Series). CRC Press.
Bohning, D., Van Der Heijden, P. G. M., & Bunge, J. (2018). Capture-recapture methods for the social and medical sciences. (Interdisciplinary Statistics Series). Chapman & Hall/CRC.
Lorenc, B., Smith, P. A., Bavdaž, M., Haraldsen, G., Nedyalkova, D., Zhang, L-C., & Zimmermann, T. (Eds.) (2018). The unit problem and other current topics in business survey methodology: Edited Proceedings of the European Establishment Statistics Workshop 2017. Cambridge Scholars Publishing. https://books.google.co.uk/books?id=WTJ3DwAAQBAJ&printsec=frontcover&dq=isbn:1527521087&hl=en&sa=X&ved=0ahUKEwjy4OTQ8vfeAhVHjqQKHfNqB10Q6AEIKjAA#v=onepage&q&f=false
Durrant, G. B., & Kreuter, F. (Eds.) (2013). Journal of the Royal Statistical Society Series A (Statistics in Society). Special Issue on Paradata. Wiley. https://doi.org/10.1111/rssa.2012.176.issue-1

Book Chapters

Van Der Heijden, P., Smith, P. A., Whittaker, J., Cruyff, M., & Bakker, B. F. M. (2019). Dual and multiple system estimation with fully and partially observed covariates. In L-C. Zhang, & R. L. Chambers (Eds.), Analysis of Integrated Data (1st ed., pp. 137-168). (Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences). Chapman and Hall/CRC.
Pelle, E., Hessen, D. J., & Van Der Heijden, P. (2018). A multidimensional Rasch model for multiple system estimation. In Capture-Recapture Methods for the Social and Medical Sciences (pp. 315-339). ( Chapman & Hall/CRC Interdisciplinary Statistics). Chapman & Hall/CRC.
Bohning, D., Bunge, J., & Van Der Heijden, P. (2018). Basic concepts of capture-recapture. In D. Boehning, P. G. M. van der Heijden, & J. Bunge (Eds.), Capture-Recapture Methods for the Social and Medical Sciences (pp. 3-17). (Interdisciplinary Statistics Series). Chapman & Hall/CRC.
Berger, Y. (2018). Empirical likelihood approaches under complex sampling designs. In Wiley StatsRef: Statistics Reference Online Wiley. https://doi.org/10.1002/9781118445112.stat08066
Bakker, B., Van Der Heijden, P. G. M., & Gerritse, S. C. (2018). Estimation of non-registered usual residents in the Netherlands. In Capture-Recapture Methods for the Social and Medical Sciences (pp. 259-273). (Chapman & Hall/CRC Interdisciplinary Statistics). Chapman & Hall/CRC.
Cruyff, M. J. L. F., Husken, T. F., & Van Der Heijden, P. G. M. (2018). Extending the truncated Poisson regression model to a time-at-risk model. In D. Boehning, P. G. M. van der Heijden, & J. Bunge (Eds.), Capture-Recapture Methods for the Social and Medical Sciences (pp. 155-162). (Chapman & Hall/CRC Interdisciplinary Statistics). Chapman & Hall/CRC.
Smith, P. A. (2018). Introduction. In B. Lorenc, P. A. Smith, M. Bavdaž, G. Haraldsen, D. Nedyalkova, L-C. Zhang, & T. Zimmermann (Eds.), The Unit Problem and Other Current Topics in Business Survey Methodology: Edited Proceedings of the European Establishment Statistics Workshop 2017 (pp. 1-6). Cambridge Scholars Publishing.
Smith, P. A., Lorenc, B., & van Delden, A. (2018). The unit problem: an overview. In B. Lorenc, P. A. Smith, M. Bavdaž, G. Haraldsen, D. Nedyalkova, L-C. Zhang, & T. Zimmermann (Eds.), The Unit Problem and Other Current Topics in Business Survey Methodology: Edited Proceedings of the European Establishment Statistics Workshop 2017 (pp. 7-17). Cambridge Scholars Publishing.
Bohning, D., Bunge, J., & Van Der Heijden, P. G. M. (2018). Uncertainty assessment in capture-recapture studies and the choice of sampling effort. In Capture-Recapture Methods for the Social and Medical Sciences (pp. 389-396). ( Chapman & Hall/CRC Interdisciplinary Statistics). Chapman & Hall/CRC.
Woods, D., & Lewis, S. (2017). Design of experiments for screening. In R. Ghanem, D. Higdon, & H. Owhadi (Eds.), Handbook of Uncertainty Quantification (1 ed.). Springer International.
Van Der Heijden, P., Cruyff, M., Whittaker, J., Bakker, B., & Smith, P. (2017). Dual and multiple system estimation: fully observed and incomplete covariates. In D. Böhning, P. van der Heijden, & J. Bunge (Eds.), Capture-Recapture Methods for the Social and Medical Sciences (pp. 213-226). (Chapman & Hall/CRC Interdisciplinary Statistics). Chapman & Hall/CRC. https://doi.org/10.4324/9781315151939
Berger, Y., Osier, G., & Goedeme, T. (2017). Standard error estimation and related sampling issues. In A. B. Atkinson, A-C. Guio, & E. Marlier (Eds.), Monitoring Social Inclusion in Europe (pp. 465-478). Publications Office of the European Union. https://doi.org/10.2785/60152, https://doi.org/10.2785/6030
Cruyff, M., Bockenholt, U., Van Der Heijden, P. G. M., & Frank, L. (2016). Chapter 18 - A Review of regression procedures for randomizedresponse data, including univariate and multivariatelogistic regression, the proportional odds model anditem response model, and self-protective responses. In A. Chaudhuri, T. C. Christofides, & C. R. Rao (Eds.), Data Gathering, Analysis and Protection of Privacy through Randomized Response Techniques: Qualitative and Quantitative Human Traits (pp. 287-315). (Handbook of Statistics; Vol. 34). Elsevier. https://doi.org/10.1016/bs.host.2016.01.016
Hinde, A., & Edgar, M. (2016). 'Following the tools': migration networks among the stone workers of Purbeck in the nineteenth century. In M. Hammond (Ed.), Rural-Urban Relationships in the Nineteenth Century: Uneasy Neighbours? (pp. 90-104). Routledge.
Deuchar, R., & Bhopal, K. (2015). Conclusions: a research agenda for researching the marginalized. In K. Bhopal, & R. Deuchar (Eds.), Researching Marginalized Groups Routledge.
van der Heijden, P. G. M., de Vries, L., Böhning, D., & Cruyff, M. (2015). Estimating the size of hard-to-reach populations using capture-recapture methodology, with a discussion of the International Labour Organization’s global estimate of forced labour. In K. Kangaspunta (Ed.), Forum on Crime and Society, Volume 8. Special Issue: Researching Hidden Populations: Approaches to and Methodologies for Generating Data on Trafficking in Persons (pp. 109-136). United Nations.
Heijden, P. G. M., Cruyff, M., & Böhning, D. (2014). Capture recapture to estimate criminal populations. In G. J. N. Bruinsma, & D. L. Weisburd (Eds.), Encyclopedia of Criminology and Criminal Justice (pp. 267-276). Springer. https://doi.org/10.1007/978-1-4614-5690-2_662
Berger, Y. G., & De La Riva Torres, O. (2014). Empirical likelihood confidence intervals: an application to the EU-SILC Household Surveys. In F. Mecatti, P. L. Conti, & M. G. Ranalli (Eds.), Contributions to Sampling Statistics (pp. 65-84). (Contributions to Sampling Statistics). Springer. https://doi.org/10.1007/978-3-319-05320-2_5
Nangsue, N., & Berger, Y. G. (2014). Optimal regression estimator for stratified two-stage sampling. In F. Mecatti, P. L. Conti, & M. G. Ranalli (Eds.), Contributions to Sampling Statistics (pp. 167-177). (Contributions to Statistics). Springer. https://doi.org/10.1007/978-3-319-05320-2_11
Durrant, G. B., D'Arrigo, J., & Mueller, G. (2013). Modeling call record data: examples from cross-sectional and longitudinal surveys. In F. Kreuter (Ed.), Improving Surveys with Paradata: Analytic Uses of Process Information (pp. 287-316). John Wiley.
Wisniowski, A., Bijak, J., Kupiszewski, M., & Kupiszewska, D. (2012). An uncertain future of immigration in Europe: insights from expert-based stochastic forecasts for selected countries. In M. Okólski (Ed.), European Immigrations: Trends, Structures and Policy Implications (pp. 211-232). (IMISCoe Research). Amsterdam University Press.

Conferences

Cruyff, M., Van Der Heijden, P., Smith, P. A., Bycroft, C., & Graham, P. (2019). Multiple system estimation for the size of the Māori population in New Zealand. Paper presented at 62nd World Statistics Congress, Kuala Lumpur, Malaysia. https://drive.google.com/file/d/1FO7Gb27IKVPXflQdtg7Z-GYu604o7tVq/view
Fréchède, B., Jolivet, E., Lecomte, C., Lemaire, T., Petit, P., & Beillas, P. (2016). A public-private integrated framework for in-silico virtual testing: the open-source PIPER project to personalize and position HBMs. Paper presented at VPH 2016: Virtual Physiological Human Conference, Netherlands.
Moreau, B., Dicko, A-H., Mailliez, P., Portejoie, P., Lecomte, C., Bah, M., Grenier, T., Jolivet, E., Petit, P., Fréchède, B., Faure, F., Gilles, B., & Beillas, P. (2016). A segmentation pipeline for the creation of statistical shape models in the PIPER project. Poster session presented at 22nd Congress of the European Society of Biomechanics (ESB 2016), France.
Berger, Y., & Oguz Alper, M. (2016). An empirical likelihood approach for complex sampling. In Proceedings Papers of the 2016 Meeting of the Statistical Society of Canada https://ssc.ca/sites/ssc/files/imce/pdf/berger_ssc2016.pdf
Kabzinska, E., Berger, Y., Pratesi, M. (Ed.), & Pena, C. (Ed.) (2016). Empirical likelihood multiplicity adjusted estimator for multiple frame surveys. Paper presented at SIS2016: 48th Scientific Meeting of the Italian Statistical Society, Italy.
Zheludev, N. (2016). Main research direction in the centre for disruptive photonic technologies. TPI Conference Singapore, Singapore.
Smith, P., Gammon, S., Cummins, S., Chatzoglou, C., & Heasman, D. (2016). Sampling procedures for assessing accuracy of record linkage. 2016 International Methodology Symposium Growth in Statistical Information: Challenges and Benefits, Gatineau, Quebec, Canada. http://www.statcan.gc.ca/eng/conferences/symposium2016/program/14729-eng.pdf
Caruana, K., & Berger, Y. G. (2015). A multivariate regression estimator for rotating sampling surveys. Paper presented at New Techniques and Technologies for Statistics, Belgium.
Lundquist, P., & Durrant, G. (2015). A test of an adaptive survey design: response propensities using length of call sequence in the Swedish Labour Force Survey (LFS). Paper presented at International Research Workshop on Bayesian Adaptive Survey Designs, Sweden.
Kabzinska, E., & Berger, Y. G. (2015). Aligning estimates from different surveys using empirical likelihood methods. Paper presented at New Techniques and Technologies for Statistics (NTTS) 2015, Belgium.
Berger, Y. G. (2015). An R Library to construct empirical likelihood confidence intervals for complex estimators. Paper presented at New Techniques and Technologies for Statistics, Belgium.
Durrant, G. (2015). Assessing nonresponse bias during fieldwork. Methodological Seminar Series, Germany.
Correa, S., Durrant, G., & Smith, P. W. F. (2015). Assessing nonresponse bias using call record data with applications to a longitudinal study. Paper presented at Conference of the International Statistical Institute (60th World Statistics Congress), Brazil.
Moore, J., Durrant, G., & Smith, P. (2015). Consent to data linkage in business surveys. Paper presented at European Survey Research Association Conference, Iceland.
Oguz Alper, M., & Berger, Y. G. (2015). Design-based confidence intervals and significance test for regression parameters using an empirical likelihood approach. Paper presented at New Techniques and Technologies for Statistics (NTTS) 2015, Belgium.
Berger, Y. G. (2015). Empirical likelihood based inference under complex sampling designs. Paper presented at Journées de Méthodologie Statistique.
Barbosa, D., Durrant, G., & Smith, P. W. F. (2015). Interviewer effects on measurement error. Paper presented at Conference of the International Statistical Institute (60th World Statistics Congress), Brazil.
Durrant, G., Maslovskaya, O., & Smith, P. WF. (2015). Modelling final outcome and length of call to improve efficiency in call scheduling. Paper presented at Conference of the International Statistical Institute (60th World Statistics Congress), Brazil.
Johansson, A., Lundquist, P., Westling, S., & Durrant, G. (2015). Modelling length and final response outcome of call sequences in the Swedish Labour Force Survey. Paper presented at 4th International Workshop on Adaptive and Responsive Survey Designs, United Kingdom.
Durrant, G. (2015). Using call record data for adaptive and responsive survey designs. Workshop of the International Leverhulme Trust Network "Bayesian Adaptive Survey Design Network", United States.
Barbosa, D., Durrant, G. B., & Smith, P. W. F. (2014). An investigation of interviewer effects on measurement error. Paper presented at Joint Statistical Meeting, United States.
Durrant, G. B., Maslovskaya, O., & Smith, P. W. F. (2014). Analysing call record data in longitudinal surveys. Paper presented at 4th Panel Survey Methods Workshop, United States.
Blom, A., & Durrant, G. B. (2014). Correlates of attrition in the German internet panel: drop-outs and sleepers. Paper presented at AAPOR2014: American Association for Public Opinion Research, United States.
Oguz Alper, M., & Berger, Y. G. (2014). Empirical likelihood confidence intervals and significance test for regression parameters under complex sampling designs. 2070-2079. Paper presented at JSM 2014 - Joint Statistical Meetings - American Statistical Association, United States.
Berger, Y. G. (2014). Empirical Likelihood Confidence Intervals under the Rao-Hartley-Cochran Sampling Design. 2053-2062. Paper presented at JSM 2014 - Joint Statistical Meetings - American Statistical Association, United States.
Durrant, G. B., Maslovskaya, O., & Smith, P. W. F. (2014). Modelling final outcome and length of call to improve efficiency in call scheduling. Paper presented at Paradata Conference: From Survey Research to Practice, United Kingdom.
Maslovskaya, O., Durrant, G. B., & Smith, P. W. F. (2014). Sequence analysis as a graphical tool for investigating call record data. Paper presented at Paradata Conference: From Survey Research to Practice, United Kingdom.
Lecomte, C. (2014). Time response of complex dynamic systems. Paper presented at ISMA 2014 International Conference on Noise and Vibration Engineering, Belgium.
Maslovskaya, O., Durrant, G. B., & Smith, P. W. F. (2014). Using sequence analysis to investigate call record data in longitudinal context. Poster session presented at British Society for Population Studies, United Kingdom.
Vassalo, R., Smith, P. W. F., & Durrant, G. B. (2013). Assessing the relative importance of current and previous interviewers on wave nonresponse in longitudinal surveys. Paper presented at European Survey Research Association Conference, Slovenia.
Maslovskaya, O., Smith, P. W. F., & Padmadas, S. S. (2013). Measuring HIV knowledge in China: simple score and latent class variable approach. Paper presented at European Survey Research Association Conference, Slovenia.
Durrant, G. B., Vassalo, R., Smith, P. W. F., & Goldstein, H. (2013). Modelling change of interviewer: effects on nonresponse in cross-sectional and longitudinal surveys. International Workshop on Explaining Interviewer Effects in Interviewer-Mediated Surveys, Germany.
Durrant, G. B., Maslovskaya, O., & Smith, P. W. F. (2013). Modelling final outcome and length of call to improve efficiency in call scheduling. International Workshop on Advances in Adaptive and Responsive Survey Design, Netherlands.
Durrant, G. B., & Schnepf, S. V. (2013). Non-response modeling in a multi-stage survey of school children (PISA). International Workshop on Advances in Adaptive and Responsive Survey Design, Netherlands.
Di Meglio, E., Osier, G., Goedeme, T., Berger, Y. G., & Di Falco, E. (2013). Standard error estimation in EU-SILC – First results of the Net-SILC2 project. Paper presented at NTTS (New Techniques and Technologies for Statistics) 2013. https://doi.org/10.2901/Eurostat.C2013.001
Durrant, G. B., Maslovskaya, O., Smith, P. W. F., & D'Arrigo, J. (2013). Using sequence analysis to better understand interviewer calling patterns: an example from the UK understanding society survey. Paper presented at European Survey Research Association Conference, Slovenia.
Durrant, G. B., Maslovskaya, O., Smith, P. W. F., & D'Arrigo, J. (2013). Using sequence analysis to better understand interviewer calling patterns: analysing the UK understanding society survey. Paper presented at International Workshop on Household Survey Nonresponse, United Kingdom.
Berger, Y. G., & Oguz Alper, M. (2013). Variance estimation of change of poverty based upon the Turkish EU-SILC survey. Paper presented at NTTS (New Techniques and Technologies for Statistics) 2013. https://doi.org/10.2901/Eurostat.C2013.001
Belloc, F., Maruotti, A., & Petrella, L. (2012). A correlated random effects model for longitudinal data with non-ignorable drop-out: an application to university student performance. In A. Di Ciaccio, M. Coli, & J. M. A. Ibanez (Eds.), Advanced Statistical Methods for the Analysis of Large Data-Sets (pp. 127-136). (Studies in Theoretical and Applied Statistics). Springer. https://doi.org/10.1007/978-3-642-21037-2_12
Vassalo, R., Durrant, G. B., & Smith, P. W. F. (2012). A simulation study of the effect of sample size and level of interpenetration on inference from cross-classified multilevel logistic regression models. Paper presented at ESRC/NCRM Research Methods Festival, United Kingdom.
D'Arrigo, J., Smith, P. W. F., & Durrant, G. B. (2012). Analysing and modelling paradata. Paper presented at E5th SRC/NCRM Research Methods Festival, United Kingdom.
Khambhaita, P., Downey, C., Muijs, D., & Kelly, A. (2012). Class, ethnicity, and exam subject entry: trends from academic year 2002 to 2009. Paper presented at Annual Meeting of the American Educational Research Association, Canada.

Articles

Books

Book Chapters

Conferences

Letter/Editorial

Reports

Reviews

Working Papers