Shrinkage to smooth non-convex cone :Principal component analysis as stein estimation

DOI10.1080/03610929908832318zbMath0918.62054OpenAlexW2008109961MaRDI QIDQ4240718

Akimichi Takemura, Satoshi Kuriki

Publication date: 7 July 1999

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

Full work available at URL: https://doi.org/10.1080/03610929908832318

zbMATH Keywords

singular value decompositionsecond fundamental formWeyl's tube formulaprincipal component analysismatrix meanreduced rank

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Ridge regression; shrinkage estimators (Lasso) (62J07) Eigenvalues, singular values, and eigenvectors (15A18)

Related Items (4)

APPLIED REGRESSION ANALYSIS BIBLIOGRAPHY UPDATE 2000–2001 ⋮ Computation of the expected Euler characteristic for the largest eigenvalue of a real non-central Wishart matrix ⋮ Improved loss estimation for a normal mean matrix ⋮ On the degrees of freedom in shrinkage estimation

Cites Work

• On estimation of a matrix of normal means with unknown covariance matrix
• Empirical Bayes minimax estimators of matrix normal means
• On estimation of matrix of normal mean
• Minimax estimators in the MANOVA model for arbitrary quadratic loss and unknown covariance matrix
• Empirical Bayes on vector observations: An extension of Stein's method
• On the Volume of Tubes
• Normal Multivariate Analysis and the Orthogonal Group

This page was built for publication: Shrinkage to smooth non-convex cone :Principal component analysis as stein estimation