Composite linear models for incomplete multinomial data.
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.
Baker
S G
SG
Biometry Branch, National Cancer Institute, Bethesda, MD 20892.
D000465:Algorithms; D000900:Anti-Bacterial Agents / Q000494:pharmacology; D015982:Bias; D003627:Data Interpretation, Statistical; D006801:Humans; D016013:Likelihood Functions; D016014:Linear Models; D008826:Microbial Sensitivity Tests / Q000706:statistics & numerical data; D011550:Pseudomonas aeruginosa / Q000187:drug effects
D000900:Anti-Bacterial Agents