Bayesian approximations in randomized response model

dc.contributorUniversidade Estadual Paulista (UNESP)
dc.creatorMigon, H. S.
dc.creatorTachibana, V. M.
dc.date2014-05-20T15:29:31Z
dc.date2016-10-25T18:04:48Z
dc.date2014-05-20T15:29:31Z
dc.date2016-10-25T18:04:48Z
dc.date1997-06-05
dc.date.accessioned2017-04-06T00:12:27Z
dc.date.available2017-04-06T00:12:27Z
dc.identifierComputational Statistics & Data Analysis. Amsterdam: Elsevier B.V., v. 24, n. 4, p. 401-409, 1997.
dc.identifier0167-9473
dc.identifierhttp://hdl.handle.net/11449/39108
dc.identifierhttp://acervodigital.unesp.br/handle/11449/39108
dc.identifier10.1016/S0167-9473(96)00075-8
dc.identifierWOS:A1997XE92300003
dc.identifierhttp://dx.doi.org/10.1016/S0167-9473(96)00075-8
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/882076
dc.descriptionPractical Bayesian inference depends upon detailed examination of posterior distribution. When the prior and likelihood are conjugate, this is easily carried out; however, in general, one must resort to numerical approximation. In this paper, our aim is to solve, using MAPLE, the Bayesian paradigm, for a very special data collecting procedure, known as the randomized-response technique. This allows researchers to obtain sensitive information while guaranteeing privacy to respondents. This approach intends to reduce false responses on sensitive questions. Exact methods and approximations will be compared from the accuracy point of view as well as for the computational effort.
dc.languageeng
dc.publisherElsevier B.V.
dc.relationComputational Statistics & Data Analysis
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectBayesian inference
dc.subjectrandomized response
dc.subjectTierney-Kadane method
dc.subjectMAPLE program
dc.titleBayesian approximations in randomized response model
dc.typeOtro


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