- 1. Some Aspects of Model-Selection Criteria
- S. L. Sclove
| 37 |
- 2. Mixture-Model Cluster Analysis Using Model Selection Criteria
and a New Informational Measure of Complexity
- H. Bozdogan
| 69 |
- 3. Information and Entropy in Cluster Analysis
- H. H. Bock
|
115 |
- 4. Information-Based Validity Functionals for Mixture Analysis
- A. C. Cutler and M. P. Windham
| 149 |
- 5. Unsupervised Classification with Stochastic Complexity
- J. Rissanen and E. S. Ristad
| 171 |
- 6. Modelling Principle Components with Structure
- B. D. Flury and B. Neuenschwander
| 183 |
- 7. AIC-Replacements for Some Multivariate Tests of Homogeneity
with Applications in Multisample Clustering and Variable Selection
- H. Bozdogan, S. L. Sclove, and A. K. Gupta
| 199 |
- 8. High Dimensional Covariance Estimation: Avoiding The Curse
of Dimensionality'
- R. M. Pruzek
| 233 |
- 9. Categorical Data Analysis by AIC
- Y. Sakamoto
| 255 |
- 10. Longitudinal Data Models with Fixed and Random Effects
- R. H. Jones
| 271 |
- 11. Multivariate Autoregressive Modeling for Analysis of
Biomedical Systems with Feedback
- T. Wada, T. Koyama, and M. Shigemori
|
293 |
- 12.A Simulation Study of Information Theoretic Techniques
and Classical Hypothesis Tests in One Factor ANOVA
- E. P. Rosenblum
| 319 |
- 13. Roles of Fisher Type Information in Latent Trait Models
- F. Samejima
| 347 |
- 14. A Review of Applications of AIC in Psychometrics
- Y. Takane
| 379 |