Yves Berger's research focuses on foundations of statistical inference from complex sample surveys. Yves Berger established a world-class research related to various fundamental issues of statistical inference from complex sample surveys. These issues include: variance estimation; repeated surveys; non-response; imputation; inference for complex parameters (empirical likelihood). His publications include articles in the Journals of the Royal Statistical Society (Series A, B and C), Biometrika and the Canadian Journal of Statistics. Yves Berger has also some research interest in econometrics, more specifically in non-linear regression with endogenous covariates.
Yves Berger teaches a wide range of courses at undergraduate and postgraduate levels: statistical modelling, generalised linear models, sample survey theory, non-response adjustment, multivariate analysis, multilevel models, longitudinal data and repeated measures, statistical computing, computer intensive statistical methods, statistical consulting, communication and research skills and demography.
Yves Berger is an Associate Editor of the Journal of the Royal Statistical Society (Series B).