Characterizing the Estimation of Parameters in Incomplete-Data Problems

Abstract A framework is given for organizing and understanding the problems of estimating the parameters of a multivariate data set which contains blocks of missing observations. The basic technique is to decompose the original estimation problem into smaller estimation problems by factoring the likelihood of the observed data into a product of likelihoods. The result is summarized in a “factorization table,” which identifies the “complete-data” factors whose parameters may be estimated using standard, well-understood complete-data techniques, and the “incomplete-data” factors whose parameters must be estimated using special missing-data methods.