The importance of complexity in model selection
type: Item
instance of: scholarly article
title: The importance of complexity in model selection
zbMATH Open document ID: 0946.62094
author: In Jae Myung
published in: Journal of Mathematical Psychology
publication date: 2000-10-29T00:00:00Z
Mathematics Subject Classification ID: 62P15
Mathematics Subject Classification ID: 91E10
Mathematics Subject Classification ID: 62B10
zbMATH DE Number: 1455926
zbMATH Keywords: model complexity
MaRDI profile type: Publication
OpenAlex ID: W2151950896
Wikidata QID: Q52080617
cites work: Q4769776
cites work: On the information-based measure of covariance complexity and its application to the evaluation of multivariate linear models
cites work: Akaike's information criterion and recent developments in information complexity
cites work: Informational complexity criteria for regression models.
cites work: Cross-validation methods
cites work: Assessing the accuracy of the maximum likelihood estimator: Observed versus expected Fisher information
cites work: A comparison of learning models
cites work: Model selection based on minimum description length
cites work: Some information measures for testing stochastic models
cites work: Bayes Factors
cites work: The Selection of Prior Distributions by Formal Rules
cites work: Logical basis for information theory and probability theory
cites work: Asymptotic properties of criteria for selection of variables in multiple regression
cites work: A universal prior for integers and estimation by minimum description length
cites work: Stochastic complexity and modeling
cites work: Stochastic complexity and the mdl principle
cites work: Estimating the dimension of a model
cites work: Q4066417
cites work: Bayesian model selection and model averaging
DOI: 10.1006/JMPS.1999.1283
description: scientific article; zbMATH DE number 1455926