{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T02:43:04Z","timestamp":1789699384097,"version":"4.0.1"},"reference-count":41,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2013,11,5]],"date-time":"2013-11-05T00:00:00Z","timestamp":1383609600000},"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":[[2014,9,1]]},"abstract":"<jats:title>Summary<\/jats:title>\n                  <jats:p>We propose the Bayesian bridge estimator for regularized regression and classification. Two key mixture representations for the Bayesian bridge model are developed: a scale mixture of normal distributions with respect to an \u03b1-stable random variable; a mixture of Bartlett\u2013Fejer kernels (or triangle densities) with respect to a two-component mixture of gamma random variables. Both lead to Markov chain Monte Carlo methods for posterior simulation, and these methods turn out to have complementary domains of maximum efficiency. The first representation is a well-known result due to West and is the better choice for collinear design matrices. The second representation is new and is more efficient for orthogonal problems, largely because it avoids the need to deal with exponentially tilted stable random variables. It also provides insight into the multimodality of the joint posterior distribution, which is a feature of the bridge model that is notably absent under ridge or lasso-type priors. We prove a theorem that extends this representation to a wider class of densities representable as scale mixtures of beta distributions, and we provide an explicit inversion formula for the mixing distribution. The connections with slice sampling and scale mixtures of normal distributions are explored. On the practical side, we find that the Bayesian bridge model outperforms its classical cousin in estimation and prediction across a variety of data sets, both simulated and real. We also show that the Markov chain Monte Carlo algorithm for fitting the bridge model exhibits excellent mixing properties, particularly for the global scale parameter. This makes for a favourable contrast with analogous Markov chain Monte Carlo algorithms for other sparse Bayesian models. All methods described in this paper are implemented in the R package BayesBridge. An extensive set of simulation results is provided in two on-line supplemental files.<\/jats:p>","DOI":"10.1111\/rssb.12042","type":"journal-article","created":{"date-parts":[[2013,11,5]],"date-time":"2013-11-05T15:42:46Z","timestamp":1383666166000},"page":"713-733","source":"Crossref","is-referenced-by-count":80,"title":["The Bayesian Bridge"],"prefix":"10.1093","volume":"76","author":[{"given":"Nicholas G.","family":"Polson","sequence":"first","affiliation":[{"name":"University of Chicago USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"James G.","family":"Scott","sequence":"additional","affiliation":[{"name":"University of Texas at Austin USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jesse","family":"Windle","sequence":"additional","affiliation":[{"name":"University of Texas at Austin USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2013,11,5]]},"reference":[{"key":"2023031306063745800_","first-page":"17","article-title":"Variational bridge regression","volume":"5","author":"Armagan","year":"2009","journal-title":"J. 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