Bayesian Estimation of Incomplete Data Using Conditionally Specified Priors

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URI: http://hdl.handle.net/10902/16289
ISSN: 0361-0918
ISSN: 1532-4141
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Autoría
Sarabia Alegría, José MaríaAutoridad Unican; Shahtahmassebi, Golnaz
Fecha
2015
Derechos
© Taylor & Francis. This an Accepted Manuscript of an article published by Taylor & Francis in Communications in Statistics - Simulation and Computation on 2015, available online: ttp://www.tandfonline.com/10.1080/03610918.2015.1091076
Publicado en
Communications in Statistics - Simulation and Computation Volume 46, 2017 - Issue 5 , Pages 3419-3435
Editorial
Taylor and Francis Inc.
Palabras clave
Conditional speci cation
Bayesian analysis
Truncated gamma distribution
Confluent hypergeometric distribution
Resumen/Abstract
ABSTRACT: In this paper, a class of conjugate prior for estimating incomplete count data based on a broad class of conjugate prior distributions is presented. The new class of prior distributions arises from a conditional perspective, making use of the conditional specification methodology and can be considered as the generalisation of the form of prior distributions that have been used previously in the estimation of incomplete count data well. Finally, some examples of simulated and real data are given.
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