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The University of Southampton
Economic, Social and Political Sciences

Parametric fractional imputation for missing data analysis Seminar

Social Statistics and Demography
9 June 2011
Room 1035, Building 2 Highfield Campus

For more information regarding this seminar, please telephone Social Statistics & Demography Division on +44 (0)23 8059 4547 or email .

Event details

Statistics Research Thursday Seminar Series

Under a parametric model for missing data, the EM algorithm is a popular tool for finding the maximum likelihood estimates (MLE) of the parameters of the model. Imputation, when carefully done, can be used to facilitate the parameter estimation by applying the complete-sample estimators to the imputed dataset. The basic idea is to generate the imputed values from the conditional distribution of the missing data given the observed data. In this article, parametric fractional imputation is proposed for generating imputed values.

Using fractional weights, the E-step of the EM algorithm can be approximated by the weighted mean of the imputed data likelihood where the fractional weights are computed from the current value of the parameter estimates. Some computational efficiency can be achieved using the idea of importance sampling and calibration weighting.

The proposed imputation method provides efficient parameter estimates for the model parameters specified in the imputation model and also provides reasonable estimates for parameters that are not part of the imputation model. Variance estimation is covered and results from a limited simulation study are presented.

Speaker information

Jae-Kwang Kim ,Iowa State University

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