Minimax estimation for certain independent component distributions under weighted squared error loss

Robert J. Miceli; William E. Strawderman

doi:10.1080/03610928608829243

Minimax estimation for certain independent component distributions under weighted squared error loss

Robert J. Miceli
, William E. Strawderman

School of Arts and Sciences, Statistics

Research output: Contribution to journal › Article › peer-review

5 Scopus citations

Abstract

Let X be a vector of independent components with mean vector θ. We assume that the distribution of the j^th component is of the form [formula omitted], i.e. a variant mixture of normal distribution. We show that certain explicit James-Stein type estimators are minimax for the problem of estimating the vector [formula omitted] under the loss function [formula omitted].

Original language	English (US)
Pages (from-to)	2191-2200
Number of pages	10
Journal	Communications in Statistics - Theory and Methods
Volume	15
Issue number	7
DOIs	https://doi.org/10.1080/03610928608829243
State	Published - Jan 1 1986

All Science Journal Classification (ASJC) codes

Statistics and Probability

Keywords

James-Stein estimation
Location parameters
decision theory

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10.1080/03610928608829243

Cite this

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abstract = "Let X be a vector of independent components with mean vector θ. We assume that the distribution of the jth component is of the form [formula omitted], i.e. a variant mixture of normal distribution. We show that certain explicit James-Stein type estimators are minimax for the problem of estimating the vector [formula omitted] under the loss function [formula omitted].",

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T1 - Minimax estimation for certain independent component distributions under weighted squared error loss

AU - Miceli, Robert J.

AU - Strawderman, William E.

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Y1 - 1986/1/1

N2 - Let X be a vector of independent components with mean vector θ. We assume that the distribution of the jth component is of the form [formula omitted], i.e. a variant mixture of normal distribution. We show that certain explicit James-Stein type estimators are minimax for the problem of estimating the vector [formula omitted] under the loss function [formula omitted].

AB - Let X be a vector of independent components with mean vector θ. We assume that the distribution of the jth component is of the form [formula omitted], i.e. a variant mixture of normal distribution. We show that certain explicit James-Stein type estimators are minimax for the problem of estimating the vector [formula omitted] under the loss function [formula omitted].

KW - James-Stein estimation

KW - Location parameters

KW - decision theory

UR - https://www.scopus.com/pages/publications/0039176206

UR - https://www.scopus.com/pages/publications/0039176206#tab=citedBy

U2 - 10.1080/03610928608829243

DO - 10.1080/03610928608829243

M3 - Article

AN - SCOPUS:0039176206

SN - 0361-0926

VL - 15

SP - 2191

EP - 2200

JO - Communications in Statistics - Theory and Methods

JF - Communications in Statistics - Theory and Methods

IS - 7

ER -

Minimax estimation for certain independent component distributions under weighted squared error loss

Abstract

All Science Journal Classification (ASJC) codes

Keywords

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