Shrinkage estimator in normal mean vector estimation based on conditional maximum likelihood estimators

Estimation of normal mean vector has broad applications such as small area estimation, estimation of nonparametric functions and estimation of wavelet coefficients. In this paper, we propose a new shrinkage estimator based on conditional maximum likelihood estimator incorporating with Stein’s risk unbiased estimator (SURE) when data have the normality. We present some theoretical work and provide numerical studies to compare with some existing methods.

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Year of publication:	2014
Authors:	Park, Junyong
Published in:	Statistics & Probability Letters. - Elsevier, ISSN 0167-7152. - Vol. 93.2014, C, p. 1-6
Publisher:	Elsevier
Subject:	Shrinkage \| Sparsity \| Empirical Bayes \| Conditional maximum likelihood estimate \| Mean vector \| Stein’s risk unbiased estimate

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Type of publication:	Article
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10010906223