A family of the information criteria using the phi-divergence for categorical data

DOI10.1016/j.csda.2018.03.001zbMath1469.62122OpenAlexW2792882170MaRDI QIDQ1662860

Haruhiko Ogasawara

Publication date: 20 August 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/10252/00005700

zbMATH Keywords

model selectionasymptotic biasAkaike information criterionriskpower divergence

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Statistical aspects of information-theoretic topics (62B10)

Related Items (1)

Asymptotic cumulants of the minimum phi-divergence estimator for categorical data under possible model misspecification

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Bias correction of the Akaike information criterion in factor analysis
• A class of cross-validatory model selection criteria
• Expected predictive least squares for model selection in covariance structures
• Asymptotic cumulants of the parameters estimators in item response theory
• A differential geometric approach to statistical inference on the basis of contrast functionals
• Minimum \(\phi\)-divergence estimator in logistic regression models
• Minimum \(\phi\)-divergence estimation in constrained latent class models for binary data
• Minimum phi-divergence estimators for loglinear models with linear constraints and multinomial sampling
• Factor analysis and AIC
• Minimum chi-square, not maximum likelihood!
• Goodness-of-fit statistics for discrete multivariate data
• Estimating the dimension of a model
• Model checking in loglinear models using \(\phi\)-divergences and MLEs
• Asymptotic divergence of estimates of discrete distributions
• On the estimation by the minimum distance method
• GLS Discrepancy Based Information Criteria for Selecting Covariance Structure Models
• Regression and time series model selection in small samples
• Further analysis of the data by Akaike's information criterion and the finite corrections
• Modified AIC and Cp in multivariate linear regression
• Minimum power-divergence estimator in three-way contingency tables
• Mean Square Error of Prediction as a Criterion for Selecting Variables
• Some Comments on C P
• Transformations Related to the Angular and the Square Root
• On Information and Sufficiency

This page was built for publication: A family of the information criteria using the phi-divergence for categorical data