Matrix quadratic risk of orthogonally invariant estimators for a normal mean matrix

05/15/2023
by   Takeru Matsuda, et al.
0

In estimation of a normal mean matrix under the matrix quadratic loss, we develop a general formula for the matrix quadratic risk of orthogonally invariant estimators. The derivation is based on several formulas for matrix derivatives of orthogonally invariant functions of matrices. As an application, we calculate the matrix quadratic risk of a singular value shrinkage estimator motivated by Stein's proposal for improving on the Efron–Morris estimator 50 years ago.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
05/27/2022

Adapting to arbitrary quadratic loss via singular value shrinkage

The Gaussian sequence model is a canonical model in nonparametric estima...
research
08/09/2019

The general Nature of Saturated Designs

In a full two-level factorial experiment the design matrix is a Hadamard...
research
05/26/2020

Matrix superharmonic priors for Bayes estimation under matrix quadratic loss

We investigate Bayes estimation of a normal mean matrix under the matrix...
research
01/16/2019

Optimal cleaning for singular values of cross-covariance matrices

We give a new algorithm for the estimation of the cross-covariance matri...
research
04/24/2023

Rectangular Rotational Invariant Estimator for General Additive Noise Matrices

We propose a rectangular rotational invariant estimator to recover a rea...
research
12/19/2022

A defect-correction algorithm for quadratic matrix equations, with applications to quasi-Toeplitz matrices

A defect correction formula for quadratic matrix equations of the kind A...
research
01/04/2021

Reconstructing Patchy Reionization with Deep Learning

The precision anticipated from next-generation cosmic microwave backgrou...

Please sign up or login with your details

Forgot password? Click here to reset