Best-subset model selection based on multitudinal assessments of likelihood improvements

DOI10.1080/02664763.2019.1645097OpenAlexW2760265670MaRDI QIDQ5861422

Knute D. Carter, Joseph E. Cavanaugh

Publication date: 1 March 2022

Published in: Journal of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02664763.2019.1645097

zbMATH Keywords

likelihood ratioAkaike information criterionBayesian information criterionlinear modelsregressionvariable selection

Mathematics Subject Classification ID

Statistical ranking and selection procedures (62F07) Applications of statistics (62Pxx)

Related Items (2)

Editorial to special issue V WCDANM 2018 ⋮ Controlling the error probabilities of model selection information criteria using bootstrapping

Uses Software

• lars

Cites Work

• Unnamed Item
• Unnamed Item
• Likelihood Ratio Tests for Model Selection and Non-Nested Hypotheses
• Best subset selection via a modern optimization lens
• A multistage algorithm for best-subset model selection based on the Kullback-Leibler discrepancy
• Selecting the best linear regression model. A classical approach
• Asymptotically efficient selection of the order of the model for estimating parameters of a linear process
• Estimating the dimension of a model
• Extending AIC to best subset regression
• Least angle regression. (With discussion)
• Resampling-based information criteria for best-subset regression
• More Comments on C P
• Can the strengths of AIC and BIC be shared? A conflict between model indentification and regression estimation
• An optimal selection of regression variables
• Regressions by Leaps and Bounds
• All Possible Regressions with Less Computation
• Some Comments on C P
• The Large-Sample Distribution of the Likelihood Ratio for Testing Composite Hypotheses
• On Information and Sufficiency
• Tests of Statistical Hypotheses Concerning Several Parameters When the Number of Observations is Large
• Maximum Likelihood Estimation of Misspecified Models
• A new look at the statistical model identification

This page was built for publication: Best-subset model selection based on multitudinal assessments of likelihood improvements