PREDICTIVE ESTIMATION OF A COVARIANCE MATRIX AND ITS STRUCTURAL PARAMETERS

DOI10.5183/jjscs.1706001_243zbMath1407.62194OpenAlexW2739396784MaRDI QIDQ4560123

Haruhiko Ogasawara

Publication date: 4 December 2018

Published in: Journal of the Japanese Society of Computational Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.5183/jjscs.1706001_243

zbMATH Keywords

mean square errorfactor analysiscovariance structureKullback-Leibler distanceexpected likelihoodpredictive estimators

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Factor analysis and principal components; correspondence analysis (62H25) Estimation in multivariate analysis (62H12) Ridge regression; shrinkage estimators (Lasso) (62J07)

Cites Work

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• Asymptotic expansions for the estimators of Lagrange multipliers and associated parameters by the maximum likelihood and weighted score methods
• Bias correction of the Akaike information criterion in factor analysis
• Optimal information criteria minimizing their asymptotic mean square errors
• Improved estimation of the covariance matrix under Stein's loss
• Estimation of a covariance matrix under Stein's loss
• Inadmissibility of non-order-preserving orthogonally invariant estimators of the covariance matrix in the case of Stein's loss
• Cross-validation methods
• Learning Regression Analysis by Simulation
• Bias Adjustment Minimizing the Asymptotic Mean Square Error
• On Optimality Properties of the Power Prior
• Ridge Regression: Biased Estimation for Nonorthogonal Problems

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