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Title: On SURE-Type Double Shrinkage Estimation
Authors: Jing, Bing-Yi
Li, Zhouping
Pan, Guangming
Zhou, Wang
Keywords: Double shrinkage estimators
Empirical Bayes
Issue Date: 2016
Source: Jing, B.-Y., Li, Z., Pan, G., & Zhou, W. (2016). On SURE-Type Double Shrinkage Estimation. Journal of the American Statistical Association, 111(516), 1696-1704.
Series/Report no.: Journal of the American Statistical Association
Abstract: The article is concerned with empirical Bayes shrinkage estimators for the heteroscedastic hierarchical normal model using Stein's unbiased estimate of risk (SURE). Recently, Xie, Kou, and Brown proposed a class of estimators for this type of problems and established their asymptotic optimality properties under the assumption of known but unequal variances. In this article, we consider this problem with unequal and unknown variances, which may be more appropriate in real situations. By placing priors for both means and variances, we propose novel SURE-type double shrinkage estimators that shrink both means and variances. Optimal properties for these estimators are derived under certain regularity conditions. Extensive simulation studies are conducted to compare the newly developed methods with other shrinkage techniques. Finally, the methods are applied to the well-known baseball dataset and a gene expression dataset. Supplementary materials for this article are available online.
ISSN: 0162-1459
DOI: 10.1080/01621459.2015.1110032
Rights: © 2016 American Statistical Association. This is the author created version of a work that has been peer reviewed and accepted for publication in Journal of the American Statistical Association, published by Taylor & Francis on behalf of American Statistical Association. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document.  The published version is available at: [].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SPMS Journal Articles

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