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Composite linear models for incomplete multinomial data
Author(s) -
Baker Stuart G.
Publication year - 1994
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.4780130522
Subject(s) - multinomial distribution , inference , computer science , missing data , maximum likelihood , homogeneity (statistics) , marginal likelihood , quasi maximum likelihood , computation , variety (cybernetics) , linear model , econometrics , statistics , expectation–maximization algorithm , mathematics , algorithm , artificial intelligence , machine learning
A composite linear model (CLM) is a matrix model for incomplete multinomial data. A CLM provides a unified approach for maximum likelihood inference which is applicable to a wide variety of problems involving incomplete multinomial data. By formulating a model as a CLM, one can simplify computation of maximum likelihood estimates and asymptotic standard errors. As an example, we use CLM to test marginal homogeneity for ordered categories, subject to both ignorable and non‐ignorable missing‐data mechanisms.