Extending AIC to best subset regression

DOI10.1007/S00180-018-0797-8zbMath1417.62191OpenAlexW2794051814MaRDI QIDQ1643010

Y. Aharonov

Publication date: 18 June 2018

Published in: Computational Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00180-018-0797-8

zbMATH Keywords

model selectionAkaike information criterionoverfittingexpected optimism

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Linear regression; mixed models (62J05) Statistical aspects of information-theoretic topics (62B10)

Related Items (1)

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

Cites Work

• Unnamed Item
• Best subset selection via a modern optimization lens
• A multistage algorithm for best-subset model selection based on the Kullback-Leibler discrepancy
• A criterion for variable selection in multiple discriminant analysis
• An improved Akaike information criterion for state-space model selection
• Estimating the dimension of a model
• Information criteria and statistical modeling.
• Estimating the Error Rate of a Prediction Rule: Improvement on Cross-Validation
• How Biased is the Apparent Error Rate of a Prediction Rule?
• Regression and time series model selection in small samples
• On Measuring and Correcting the Effects of Data Mining and Model Selection
• Selection of the order of an autoregressive model by Akaike's information criterion
• Further analysis of the data by Akaike's information criterion and the finite corrections
• Adjusted Coefficients of Determination for Logistic Regression
• Maximum Likelihood Estimation of Misspecified Models
• A new look at the statistical model identification

This page was built for publication: Extending AIC to best subset regression