Information theoretic criterion approach to dimensionality reduction in multinomial logistic regression models. part i: theory

DOI10.1080/03610928908830024zbMath0696.62232OpenAlexW2078335201MaRDI QIDQ3473229

N. Sambamoorthi

Publication date: 1989

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610928908830024

zbMATH Keywords

maximum likelihood estimationmodel selectionAkaike information criterionstrong consistencyrank estimationselection of variablescollapsibility of responsesqualitative responses

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Hypothesis testing in multivariate analysis (62H15) Statistical aspects of information-theoretic topics (62B10)

Related Items (1)

Information Theoretic Criterion Approach to Dimensionality Reduction in Multinomial Logistic Regression Models: Part II: Some Simulations¹

Cites Work

• Unnamed Item
• Cox's regression model for counting processes: A large sample study
• Model selection and Akaike's information criterion (AIC): The general theory and its analytical extensions
• On the choice of a model to fit data from an exponential family
• Estimating the dimension of a model
• On detection of the number of signals in presence of white noise
• Linear Statistical Inference and its Applications
• A new look at the statistical model identification

This page was built for publication: Information theoretic criterion approach to dimensionality reduction in multinomial logistic regression models. part i: theory