activity
20052026
most citedCombining information from independent sources through confidence distributions

142 citations · 479 across the 31 of their papers we have counts for

collaborators

33 papers

math.ST2026

Proper Bayes minimax multiple shrinkage estimation

Pankaj Bhagwat, William E. Strawderman, Edward I. George

For the canonical problem of estimating a multivariate normal mean under squared error loss, we demonstrate, for the first time, the existence of proper Bayes minimax multiple shri…

math.ST2025

Constructing Bayes Minimax Estimators through Integral Transformations

Dominique Fourdrinier, William E. Strawderman, Martin T. Wells

The problem of Bayes minimax estimation for the mean of a multivariate normal distribution under quadratic loss has attracted significant attention recently. These estimators have…

math.ST2023

Double shrinkage priors for a normal mean matrix

Takeru Matsuda, Fumiyasu Komaki, William E. Strawderman

We consider estimation of a normal mean matrix under the Frobenius loss. Motivated by the Efron--Morris estimator, a generalization of Stein's prior has been recently developed, wh…

math.ST2021

A Review of Brown 1971 (in)admissibility results under scale mixtures of Gaussian priors

Yuzo Maruyama, William, E. Strawderman

Brown's 1971 paper "Admissible estimators, recurrent diffusions and insoluble boundary value problems" is a landmark in the admissibility literature. It nearly completely settles t…

math.ST2021★ 1 cited

On admissible estimation of a mean vector when the scale is unknown

Yuzo Maruyama, William E. Strawderman

We consider admissibility of generalized Bayes estimators of the mean of a multivariate normal distribution when the scale is unknown under quadratic loss. The priors considered pu…

math.ST2020

Estimation under matrix quadratic loss and matrix superharmonicity

Takeru Matsuda, William E. Strawderman

We investigate estimation of a normal mean matrix under the matrix quadratic loss. Improved estimation under the matrix quadratic loss implies improved estimation of any linear com…