Simple Fitting Algorithms for Incomplete Categorical Data

Journal of the Royal Statistical Society: Series B (Statistical Methodology) · 1997 · Volume 59 · Issue 2

Abstract

A popular approach to estimation based on incomplete data is the EM algorithm. For categorical data, this paper presents a simple expression of the observed data log-likelihood and its derivatives in terms of the complete data for a broad class of models and missing data patterns. We show that using the observed data likelihood directly is easy and has some advantages. One can gain considerable computational speed over the EM algorithm and a straightforward variance estimator is obtained for the parameter estimates. The general formulation treats a wide range of missing data problems in a uniform way. Two examples are worked out in full.

Keywords

coarsened data, EM algorithm, Fisher scoring algorithm, generalized linear models, longitudinal data, maximum likelihood estimation, missing values, multivariate categorical data, repeated measures

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