Consistent variable selection criteria in multivariate linear regression even when dimension exceeds sample size

DOI10.32917/hmj/1607396493zbMath1469.62423OpenAlexW3150317560MaRDI QIDQ2041755

Ryoya Oda

Publication date: 23 July 2021

Published in: Hiroshima Mathematical Journal (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.32917/hmj/1607396493

zbMATH Keywords

non-normalityselection consistencymultivariate linear regressionhybrid-ultra-high-dimensional asymptotic frameworkvariable selection criterion

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Linear regression; mixed models (62J05) Statistical aspects of big data and data science (62R07)

Related Items (1)

High-dimensional consistencies of KOO methods in multivariate regression model and discriminant analysis

Cites Work

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• Consistency of high-dimensional AIC-type and \(C_p\)-type criteria in multivariate linear regression
• Lasso penalized model selection criteria for high-dimensional multivariate linear regression analysis
• The commutation matrix: Some properties and applications
• Prediction error criterion for selecting variables in a linear regression model
• Model selection and Akaike's information criterion (AIC): The general theory and its analytical extensions
• Strong consistency of the information criterion for model selection in multivariate analysis
• Estimating the dimension of a model
• On detection of the number of signals in presence of white noise
• Optimization of ridge parameters in multivariate generalized ridge regression by plug-in methods
• Distance-based classifier by data transformation for high-dimension, strongly spiked eigenvalue models
• Estimations for some functions of covariance matrix in high dimension under non-normality and its applications
• A consistency property of the AIC for multivariate linear models when the dimension and the sample size are large
• Conditions for Consistency of a Log-Likelihood-Based Information Criterion in Normal Multivariate Linear Regression Models under the Violation of the Normality Assumption
• Selection of Variables in Multivariate Regression Models for Large Dimensions
• More Comments on C P
• The multivariate C_p
• Modified AIC and Cp in multivariate linear regression
• Applied Multivariate Analysis
• Two-sample tests for high-dimension, strongly spiked eigenvalue models
• A survey of high dimension low sample size asymptotics
• Asymptotic Results of a High Dimensional MANOVA Test and Power Comparison When the Dimension is Large Compared to the Sample Size
• A Significance Test for the Separation of Two Highly Multivariate Small Samples
• A High Dimensional Two Sample Significance Test
• Some Comments on C P
• A new look at the statistical model identification

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