Mallows criterion for heteroskedastic linear regressions with many regressors

DOI10.1016/J.ECONLET.2021.109864zbMath1467.62121OpenAlexW3156697856MaRDI QIDQ2036956

Stanislav Anatolyev

Publication date: 30 June 2021

Published in: Economics Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.econlet.2021.109864

zbMATH Keywords

model selectionconditional heteroskedasticityMallows criterionmany regressors

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Statistical ranking and selection procedures (62F07)

Cites Work

• Asymptotic optimality of generalized \(C_ L\), cross-validation, and generalized cross-validation in regression with heteroskedastic errors
• Asymptotic optimality for \(C_ p\), \(C_ L\), cross-validation and generalized cross-validation: Discrete index set
• Inference in Linear Regression Models with Many Covariates and Heteroscedasticity
• ALTERNATIVE ASYMPTOTICS AND THE PARTIALLY LINEAR MODEL WITH MANY REGRESSORS
• Leave‐Out Estimation of Variance Components
• Two-Step Estimation and Inference with Possibly Many Included Covariates
• Heteroscedasticity‐robustCpmodel averaging
• A modern maximum-likelihood theory for high-dimensional logistic regression
• Some Comments on C P

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