Multiply Imputing Missing Values in Data Sets with Mixed Measurement Scales Using a Sequence of Generalized Linear Models

• Activity Number: 92
• Type: Invited
• Date/Time: Sunday, August 4, 2013 : 8:30 PM to 10:30 PM
• Sponsor: Social Statistics Section
• Author(s): Robin Mitra*+ and Min Lee
• Companies: University of Southampton and University of Southampton
• Keywords: Data Augmentation ; Latent variable ; Missing data ; Multiple imputation

Abstract

Multiple imputation is a commonly used approach to deal with missing values. In this approach, missing values are imputed by taking draws from the posterior predictive distribution for the missing values conditional on the observed values. In order to preserve the statistical properties present in the data, one must use a plausible distribution to generate the imputed values. In data sets containing variables with different measurement scales, e.g. some categorical and some continuous variables, Multivariate Imputation by Chained Equations (MICE) is a commonly used multiple imputation method. However, imputations from such an approach are not necessarily drawn from a proper posterior predictive distribution. We propose a method to multiply impute missing values in such data sets by modelling the joint distribution of the variables in the data through a sequence of generalised linear models, and use data augmentation methods to draw imputations from a proper posterior distribution using Markov Chain Monte Carlo (MCMC). We compare the performance of our method with MICE using simulation studies and on a genuine data set taken from a breast feeding study.