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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/328

Title: Simple fitting algorithms for incomplete categorical data
Authors: Molenberghs, Geert
Goetghebeur, Els J.T.
Keywords: Categorical data
Multivariate data
Longitudinal data
Issue Date: 1997
Citation: Journal of the Royal Statistical Society, series B, 59 (2), p. 401-414
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
URI: http://hdl.handle.net/1942/328
DOI: 10.1111/1467-9868.00075
ISI #: A1997WP77100006
ISSN: 1369-7412
Type: Journal Contribution
Appears in Collections: Research publications

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