{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T11:24:20Z","timestamp":1764588260384},"reference-count":14,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2022,2,1]],"date-time":"2022-02-01T00:00:00Z","timestamp":1643673600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,11,29]]},"abstract":"<jats:title>Summary<\/jats:title>\n               <jats:p>A Bayesian prediction problem for the two-dimensional Wishart model is investigated within the framework of decision theory. The loss function is the Kullback\u2013Leibler divergence. We construct a scale-invariant and permutation-invariant prior distribution that shrinks the correlation coefficient. The prior is the geometric mean of the right invariant prior with respect to permutation of the indices, and is characterized by a uniform distribution for Fisher\u2019s $z$-transformation of the correlation coefficient. The Bayesian predictive density based on the prior is shown to be minimax.<\/jats:p>","DOI":"10.1093\/biomet\/asac006","type":"journal-article","created":{"date-parts":[[2022,1,26]],"date-time":"2022-01-26T12:14:41Z","timestamp":1643199281000},"page":"1173-1180","source":"Crossref","is-referenced-by-count":2,"title":["A correlation-shrinkage prior for Bayesian prediction of the two-dimensional Wishart model"],"prefix":"10.1093","volume":"109","author":[{"given":"T","family":"Sei","sequence":"first","affiliation":[{"name":"Graduate School of Information Science and Technology, The University of Tokyo , 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"F","family":"Komaki","sequence":"additional","affiliation":[{"name":"Graduate School of Information Science and Technology, The University of Tokyo , 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2022,2,1]]},"reference":[{"key":"2022112917593695200_B1","first-page":"1281","article-title":"Modeling covariance matrices in terms of standard deviations and correlations, with application to shrinkage","volume":"10","author":"Barnard,","year":"2000","journal-title":"Statist. 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