Information Criterion Model Selection Research Articles

Model Selection Information Criteria for Non-Nested Latent Class Models

Latent class models have been developed for assessment of hierarchic relations in scaling and behavioral analysis. This article investigated the use of three model selection information criteria—Akaike AIC, Schwarz SIC, and Bozdogan CAIC—for non-nested models. In general, SIC and CAIC were superior to AIC for relatively simple models, whereas AIC was superior for more complex models, although accuracy was often quite low for such models. In addition, some effects were detected for error rates in the models.

Model Selection Information Criteria for Non-Nested Latent Class Models

Model Selection Information Criteria for Non-Nested Latent Class Models

Consistency of a Class of Information Criteria for Model Selection in Nonlinear Regression

Article DataHistoryPublished online: 28 July 2006Publication DataISSN (print): 0040-585XISSN (online): 1095-7219Publisher: Society for Industrial and Applied MathematicsCODEN: tprbau

Consistency of a class of information criteria for model selection in non-linear regression

In this paper we prove the consistency in probability of a class of generalized BIC criteria for model selection in non-linear regression, by using asymptotic results of Gallant. This extends a result obtained by Nishii for model selection in linear regression.

Strong consistency of the information criterion for model selection in multivariate analysis

Strong consistency of the information criterion for model selection in multivariate analysis