A high-dimensional bias-corrected AIC for selecting response variables in multivariate calibration

DOI10.1080/03610926.2019.1705978OpenAlexW3000047909MaRDI QIDQ5078559

Yoshie Mima, Ryoya Oda, Yasunori Fujikoshi, Hirokazu Yanagihara

Publication date: 23 May 2022

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610926.2019.1705978

zbMATH Keywords

AICbias correctionmultivariate calibrationhigh-dimensional criterion

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Statistics (62-XX) Statistical ranking and selection procedures (62F07)

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
• Selection of variables in a multivariate inverse regression problem
• Jackknife bias correction of the AIC for selecting variables in canonical correlation analysis under model misspecification
• Asymptotic expansions of the distributions of MANOVA test statistics when the dimension is large
• Regression and time series model selection in small samples
• Further analysis of the data by Akaike's information criterion and the finite corrections
• Model Selection for Multivariate Regression in Small Samples
• Multivariate Statistics

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