Singular Value Shrinkage Priors for Bayesian Prediction

Takeru Matsuda; Fumiyasu Komaki

doi:10.1093/biomet/asv036

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arXiv · 1408.2951

Singular Value Shrinkage Priors for Bayesian Prediction

Takeru Matsuda·Fumiyasu Komaki

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Abstract

We develop singular value shrinkage priors for the mean matrix parameters in the matrix-variate normal model with known covariance matrices. Our priors are superharmonic and put more weight on matrices with smaller singular values. They are a natural generalization of the Stein prior. Bayes estimators and Bayesian predictive densities based on our priors are minimax and dominate those based on the uniform prior in finite samples. In particular, our priors work well when the true value of the parameter has low rank.

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BibTeX RIS

Takeru Matsuda, Fumiyasu Komaki. 2014-08-13. Singular Value Shrinkage Priors for Bayesian Prediction. https://doi.org/10.1093/biomet%2Fasv036

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