Back to Search
Start Over
Composite linear models for incomplete multinomial data
- Source :
- Statistics in medicine. 13(5-7)
- Publication Year :
- 1994
-
Abstract
- 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.
- Subjects :
- Statistics and Probability
Likelihood Functions
Epidemiology
Maximum likelihood
Computation
Linear model
Inference
Marginal homogeneity
Microbial Sensitivity Tests
Matrix model
Anti-Bacterial Agents
Standard error
Bias
Data Interpretation, Statistical
Statistics
Pseudomonas aeruginosa
Linear Models
Applied mathematics
Humans
Multinomial distribution
Algorithms
Mathematics
Subjects
Details
- ISSN :
- 02776715
- Volume :
- 13
- Issue :
- 5-7
- Database :
- OpenAIRE
- Journal :
- Statistics in medicine
- Accession number :
- edsair.doi.dedup.....7b2e42e0a58fe289a9ffa6a7b17c8990