Composite linear models for incomplete multinomial data
Article first published online: 15 OCT 2006
Copyright © 1994 John Wiley & Sons, Ltd.
Statistics in Medicine
Volume 13, Issue 5-7, pages 609–622, 15 March - 15 April 1994
How to Cite
Baker, S. G. (1994), Composite linear models for incomplete multinomial data. Statist. Med., 13: 609–622. doi: 10.1002/sim.4780130522
- Issue published online: 15 OCT 2006
- Article first published online: 15 OCT 2006
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.