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Simple Fitting Algorithms for Incomplete Categorical Data
Author(s) -
Molenberghs Geert,
Goetghebeur Els
Publication year - 1997
Publication title -
journal of the royal statistical society: series b (statistical methodology)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.523
H-Index - 137
eISSN - 1467-9868
pISSN - 1369-7412
DOI - 10.1111/1467-9868.00075
Subject(s) - categorical variable , missing data , estimator , simple (philosophy) , algorithm , range (aeronautics) , computer science , variance (accounting) , maximum likelihood , expectation–maximization algorithm , data mining , mathematics , statistics , machine learning , philosophy , epistemology , materials science , accounting , business , composite material
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.