{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T09:55:59Z","timestamp":1790762159171,"version":"4.1.0"},"reference-count":27,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2020,9,14]],"date-time":"2020-09-14T00:00:00Z","timestamp":1600041600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,5,15]]},"abstract":"<jats:title>Summary<\/jats:title>\n               <jats:p>We study posterior contraction rates in sparse high-dimensional generalized linear models using priors incorporating sparsity. A mixture of a point mass at zero and a continuous distribution is used as the prior distribution on regression coefficients. In addition to the usual posterior, the fractional posterior, which is obtained by applying Bayes theorem with a fractional power of the likelihood, is also considered. The latter allows uniformity in posterior contraction over a larger subset of the parameter space. In our set-up, the link function of the generalized linear model need not be canonical. We show that Bayesian methods achieve convergence properties analogous to lasso-type procedures. Our results can be used to derive posterior contraction rates in many generalized linear models including logistic, Poisson regression and others.<\/jats:p>","DOI":"10.1093\/biomet\/asaa074","type":"journal-article","created":{"date-parts":[[2020,9,1]],"date-time":"2020-09-01T19:36:37Z","timestamp":1598988997000},"page":"367-379","source":"Crossref","is-referenced-by-count":23,"title":["Posterior contraction in sparse generalized linear models"],"prefix":"10.1093","volume":"108","author":[{"given":"Seonghyun","family":"Jeong","sequence":"first","affiliation":[{"name":"Department of Statistics and Data Science, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Subhashis","family":"Ghosal","sequence":"additional","affiliation":[{"name":"Department of Statistics, North Carolina State University, 5109 SAS Hall, 2311 Stinson Drive, Raleigh, North Carolina 27695, U.S.A"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2020,9,14]]},"reference":[{"key":"2021051519491663100_B1","doi-asserted-by":"crossref","first-page":"3721","DOI":"10.1109\/TIT.2016.2555812","article-title":"Model selection and minimax estimation in generalized linear models","volume":"62","author":"Abramovich,","year":"2016","journal-title":"IEEE Trans. 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