Bozdogan Proceedings of the First US/Japan Conference on the Frontiers of Statistical Modeling: An Informational Approach

Volume 2 Multivariate Statistical Modeling
Erscheinungsjahr 2012
ISBN: 978-94-011-0800-3
Verlag: Springer Netherland
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 417 Seiten, eBook

Often a statistical analysis involves use of a set of alternative models for the data. A "model-selection criterion" is a formula which provides a figure-of merit for the alternative models. Generally the alternative models will involve different numhers of parameters. Model-selection criteria take into account hoth the goodness-or-fit of a model and the numher of parameters used to achieve that fit. 1.1. SETS OF ALTERNATIVE MODELS Thus the focus in this paper is on data-analytic situations ill which there is consideration of a set of alternative models. Choice of a suhset of explanatory variahles in regression, the degree of a polynomial regression, the number of factors in factor analysis, or the numher of dusters in duster analysis are examples of such situations. 1.2. MODEL SELECTION VERSUS HYPOTHESIS TESTING In exploratory data analysis or in a preliminary phase of inference an approach hased on model-selection criteria can offer advantages over tests of hypotheses. The model-selection approach avoids the prohlem of specifying error rates for the tests. With model selection the focus can he on simultaneous competition between a hroad dass of competing models rather than on consideration of a sequence of simpler and simpler models.

Zielgruppe

Research

Autoren/Hrsg.

Bozdogan, H.

Weitere Mitwirkende

Sclove, S.L.

Gupta, Arjun K.

Haughton, D.

Kitagawa, G.

Ozaki, T.

Tanabe, Kunio

Inhaltsverzeichnis

of Volume 2.- Summary of Contributed Papers to Volume 2.- 1. Some Aspects of Model-Selection Criteria.- 2. Mixture-Model Cluster Analysis Using Model Selection Criteria and a New Informational Measure of Complexity.- 3. Information and Entropy in Cluster Analysis.- 4. Information-Based Validity Functionals for Mixture Analysis.- 5. Unsupervised Classification with Stochastic Complexity.- 6. Modelling Principal Components with Structure.- 7. AIC-Replacements for Some Multivariate Tests of Homogeneity with Applications in Multisample Clustering and Variable Selection.- 8. High Dimensional Covariance Estimation: ‘Avoiding The Curse of Dimensionality’.- 9. Categorical Data Analysis by AIC.- 10. Longitudinal Data Models with Fixed and Random Effects.- 11. Multivariate Autoregressive Modeling for Analysis of Biomedical Systems with Feedback.- 12. A Simulation Study of Information Theoretic Techniques an Hypothesis Tests in One Factor ANOVA.- 13. Roles of Fisher Type Information in Latent Trait Models.- 14. A Review of Applications of AIC in Psychometrics.- Index to Volume 2.