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Shrinkage estimator in normal mean vector estimation based on conditional maximum likelihood estimators
Institution:1. Faculty of Materials Science and Chemical Engineering, Ningbo University, No. 818 Fenghua Road, Ningbo 315211, PR China;2. Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao 266101, PR China;3. School of Materials and Chemical Engineering, Ningbo University of Technology, 315211, PR China;1. Department of Mathematics & Statistics, University of Ottawa, 585 King Edward, Ottawa ON K1N 6N5, Canada;2. School of Mathematics & Statistics, University of Sydney, NSW 2006, Australia;1. Department of Mathematics, University of Beira Interior, Covilhã, Portugal;2. Center of Mathematics of Minho University, Braga, Portugal;1. Department of Statistics, Central China Normal University, Wuhan 430079, China;2. Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, Egypt
Abstract: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.
Keywords:Shrinkage  Sparsity  Empirical Bayes  Conditional maximum likelihood estimate  Mean vector  Stein’s risk unbiased estimate
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