{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T04:24:15Z","timestamp":1750307055153,"version":"3.41.0"},"reference-count":24,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2013,1,1]],"date-time":"2013-01-01T00:00:00Z","timestamp":1356998400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Model. Comput. Simul."],"published-print":{"date-parts":[[2013,1]]},"abstract":"<jats:p>This article deals with binomial logit models where the parameters are estimated within a Bayesian framework. Such models arise, for instance, when repeated measurements are available for identical covariate patterns. To perform MCMC sampling, we rewrite the binomial logit model as an augmented model which involves some latent variables called random utilities. It is straightforward, but inefficient, to use the individual random utility model representation based on the binary observations reconstructed from each binomial observation. Alternatively, we present in this article a new method to aggregate the random utilities for each binomial observation. Based on this aggregated representation, we have implemented an independence Metropolis-Hastings sampler, an auxiliary mixture sampler, and a novel hybrid auxiliary mixture sampler. A comparative study on five binomial datasets shows that the new aggregation method leads to a superior sampler in terms of efficiency compared to previously published data augmentation samplers.<\/jats:p>","DOI":"10.1145\/2414416.2414419","type":"journal-article","created":{"date-parts":[[2013,1,29]],"date-time":"2013-01-29T16:20:55Z","timestamp":1359476455000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Efficient MCMC for Binomial Logit Models"],"prefix":"10.1145","volume":"23","author":[{"given":"Agnes","family":"Fussl","sequence":"first","affiliation":[{"name":"Johannes Kepler University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sylvia","family":"Fr\u00fchwirth-Schnatter","sequence":"additional","affiliation":[{"name":"University of Economics and Business"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rudolf","family":"Fr\u00fchwirth","sequence":"additional","affiliation":[{"name":"Austrian Academy of Sciences"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2013,1]]},"reference":[{"doi-asserted-by":"publisher","key":"e_1_2_1_1_1","DOI":"10.1080\/01621459.1993.10476321"},{"doi-asserted-by":"publisher","key":"e_1_2_1_2_1","DOI":"10.2307\/2346223"},{"volume-title":"Handbook of the Logistic Distribution","author":"Cutler C. D.","key":"e_1_2_1_3_1"},{"doi-asserted-by":"publisher","key":"e_1_2_1_4_1","DOI":"10.1007\/s00362-006-0296-9"},{"doi-asserted-by":"publisher","key":"e_1_2_1_5_1","DOI":"10.1007\/s00362-006-0039-y"},{"doi-asserted-by":"crossref","unstructured":"Fahrmeir L. and Tutz G. 2001. Multivariate Statistical Modelling based on Generalized Linear Models 2nd Ed. Springer Series in Statistics. Springer Berlin. Fahrmeir L. and Tutz G. 2001. Multivariate Statistical Modelling based on Generalized Linear Models 2nd Ed. Springer Series in Statistics. Springer Berlin.","key":"e_1_2_1_6_1","DOI":"10.1007\/978-1-4757-3454-6"},{"doi-asserted-by":"publisher","key":"e_1_2_1_7_1","DOI":"10.1016\/j.csda.2006.10.006"},{"doi-asserted-by":"crossref","unstructured":"Fr\u00fchwirth-Schnatter S. and Fr\u00fchwirth R. 2010. Data augmentation and MCMC for binary and multinomial logit models. In Statistical Modelling and Regression Structures -- Festschrift in Honour of Ludwig Fahrmeir T. Kneib and G. Tutz Eds. Physica-Verlag Heidelberg 111--132. Fr\u00fchwirth-Schnatter S. and Fr\u00fchwirth R. 2010. Data augmentation and MCMC for binary and multinomial logit models. In Statistical Modelling and Regression Structures -- Festschrift in Honour of Ludwig Fahrmeir T. Kneib and G. Tutz Eds. Physica-Verlag Heidelberg 111--132.","key":"e_1_2_1_8_1","DOI":"10.1007\/978-3-7908-2413-1_7"},{"doi-asserted-by":"publisher","key":"e_1_2_1_9_1","DOI":"10.17713\/ajs.v41i1.186"},{"doi-asserted-by":"publisher","key":"e_1_2_1_10_1","DOI":"10.1007\/s11222-008-9109-4"},{"doi-asserted-by":"publisher","key":"e_1_2_1_11_1","DOI":"10.1093\/biomet\/93.4.827"},{"doi-asserted-by":"publisher","key":"e_1_2_1_12_1","DOI":"10.1214\/ss\/1177011137"},{"doi-asserted-by":"publisher","key":"e_1_2_1_13_1","DOI":"10.1214\/12-BA719"},{"doi-asserted-by":"crossref","unstructured":"Hilbe J. M. 2007. Negative Binomial Regression. Cambridge University Press Cambridge. Hilbe J. M. 2007. Negative Binomial Regression . Cambridge University Press Cambridge.","key":"e_1_2_1_14_1","DOI":"10.1017\/CBO9780511811852"},{"doi-asserted-by":"publisher","key":"e_1_2_1_15_1","DOI":"10.1214\/06-BA105"},{"key":"e_1_2_1_16_1","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1080\/00031305.1998.10480547","article-title":"Markov chain Monte Carlo in practice: A roundtable discussion","volume":"52","author":"Kass R. E.","year":"1998","journal-title":"Amer. Statist."},{"volume-title":"Frontiers of Econometrics","author":"McFadden D.","key":"e_1_2_1_17_1"},{"doi-asserted-by":"publisher","key":"e_1_2_1_18_1","DOI":"10.1016\/j.jempfin.2006.05.002"},{"doi-asserted-by":"publisher","key":"e_1_2_1_19_1","DOI":"10.1198\/016214508000000337"},{"doi-asserted-by":"publisher","key":"e_1_2_1_20_1","DOI":"10.1007\/s00362-009-0205-0"},{"doi-asserted-by":"publisher","key":"e_1_2_1_21_1","DOI":"10.1093\/biomet\/81.1.115"},{"doi-asserted-by":"publisher","key":"e_1_2_1_22_1","DOI":"10.1198\/106186008X289849"},{"doi-asserted-by":"publisher","key":"e_1_2_1_23_1","DOI":"10.1016\/j.csda.2011.06.033"},{"unstructured":"Zelterman D. and Balakrishnan N. 1992. Univariate generalized logistic distributions. In Handbook of the Logistic Distribution N. Balakrishnan Ed. Marcel Dekker New York 209--221. Zelterman D. and Balakrishnan N. 1992. Univariate generalized logistic distributions. In Handbook of the Logistic Distribution N. Balakrishnan Ed. Marcel Dekker New York 209--221.","key":"e_1_2_1_24_1"}],"container-title":["ACM Transactions on Modeling and Computer Simulation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/2414416.2414419","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/2414416.2414419","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T09:21:10Z","timestamp":1750238470000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/2414416.2414419"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2013,1]]},"references-count":24,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2013,1]]}},"alternative-id":["10.1145\/2414416.2414419"],"URL":"https:\/\/doi.org\/10.1145\/2414416.2414419","relation":{},"ISSN":["1049-3301","1558-1195"],"issn-type":[{"type":"print","value":"1049-3301"},{"type":"electronic","value":"1558-1195"}],"subject":[],"published":{"date-parts":[[2013,1]]},"assertion":[{"value":"2011-10-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2012-06-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2013-01-01","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}