Simple Fitting Algorithms for Incomplete Categorical Data

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...

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Bibliographic Details
Published inJournal of the Royal Statistical Society. Series B, Statistical methodology Vol. 59; no. 2; pp. 401 - 414
Main Authors Molenberghs, Geert, Goetghebeur, Els
Format Journal Article
LanguageEnglish
Published Oxford, UK and Boston, USA Blackwell Publishers Ltd 1997
Subjects
Algorithms
coarsened data
EM algorithm
Fisher scoring algorithm
generalized linear models
Linear models
longitudinal data
maximum likelihood estimation
Measurement
missing values
multivariate categorical data
Probability
repeated measures
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Summary: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.
Bibliography:ark:/67375/WNG-6C31J91N-S
ArticleID:RSSB075
istex:6411ED950E6DACA9FCDCC288BCF7A243FBCE046E
ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:1369-7412
1467-9868
DOI:10.1111/1467-9868.00075