{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T23:25:30Z","timestamp":1784762730727,"version":"3.55.0"},"reference-count":29,"publisher":"Oxford University Press (OUP)","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,9,1]]},"abstract":"<jats:title>Summary<\/jats:title><jats:p>For the most popular discrete nonparametric models, beyond the Dirichlet process, the prior guess at the shape of the data-generating distribution, also known as the base measure, is assumed to be diffuse. Such a specification greatly simplifies the derivation of analytical results, allowing for a straightforward implementation of Bayesian nonparametric inferential procedures. However, in several applied problems the available prior information leads naturally to the incorporation of an atom into the base measure, and then the Dirichlet process is essentially the only tractable choice for the prior. In this paper we fill this gap by considering the Pitman\u2013Yor process with an atom in its base measure. We derive computable expressions for the distribution of the induced random partitions and for the predictive distributions. These findings allow us to devise an effective generalized P\u00f3lya urn Gibbs sampler. Applications to density estimation, clustering and curve estimation, with both simulated and real data, serve as an illustration of our results and allow comparisons with existing methodology. In particular, we tackle a functional data analysis problem concerning basal body temperature curves.<\/jats:p>","DOI":"10.1093\/biomet\/asx041","type":"journal-article","created":{"date-parts":[[2017,7,3]],"date-time":"2017-07-03T11:09:41Z","timestamp":1499080181000},"page":"681-697","source":"Crossref","is-referenced-by-count":33,"title":["On the Pitman\u2013Yor process with spike and slab base measure"],"prefix":"10.1093","volume":"104","author":[{"given":"A.","family":"Canale","sequence":"first","affiliation":[{"name":"Department of Statistical Sciences, University of Padua, Via C. Battisti 241, 35121 Padua, Italy canale@stat.unipd.it"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"A.","family":"Lijoi","sequence":"additional","affiliation":[{"name":"Department of Decision Sciences, Bocconi University, via R\u00f6ntgen 1, 20136 Milan, Italy lijoi@unibocconi.it"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"B.","family":"Nipoti","sequence":"additional","affiliation":[{"name":"School of Computer Science and Statistics, Trinity College, College Green, Dublin 2, Ireland nipotib@tcd.ie"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"I.","family":"Pr\u00fcnster","sequence":"additional","affiliation":[{"name":"Department of Decision Sciences, Bocconi University, via R\u00f6ntgen 1, 20136 Milan, Italy igor@unibocconi.it"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2017,8,3]]},"reference":[{"key":"2019071815502049500_B1","doi-asserted-by":"crossref","first-page":"1373","DOI":"10.1002\/sim.6786","article-title":"Variable selection in covariate dependent random partition models: An application to urinary tract infection.","volume":"35","author":"Barcella","year":"2016","journal-title":"Statist. 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