Bayesian analysis of skew-t multivariate null intercept measurement error model

dc.creatorLACHOS, Victor H.
dc.creatorCANCHO, Vicente G.
dc.creatorAOKI, Reiko
dc.date.accessioned2012-10-20T03:34:44Z
dc.date.accessioned2018-07-04T15:38:34Z
dc.date.available2012-10-20T03:34:44Z
dc.date.available2018-07-04T15:38:34Z
dc.date.created2012-10-20T03:34:44Z
dc.date.issued2010
dc.identifierSTATISTICAL PAPERS, v.51, n.3, p.531-545, 2010
dc.identifier0932-5026
dc.identifierhttp://producao.usp.br/handle/BDPI/28909
dc.identifier10.1007/s00362-008-0138-z
dc.identifierhttp://dx.doi.org/10.1007/s00362-008-0138-z
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1625551
dc.description.abstractThe multivariate skew-t distribution (J Multivar Anal 79:93-113, 2001; J R Stat Soc, Ser B 65:367-389, 2003; Statistics 37:359-363, 2003) includes the Student t, skew-Cauchy and Cauchy distributions as special cases and the normal and skew-normal ones as limiting cases. In this paper, we explore the use of Markov Chain Monte Carlo (MCMC) methods to develop a Bayesian analysis of repeated measures, pretest/post-test data, under multivariate null intercept measurement error model (J Biopharm Stat 13(4):763-771, 2003) where the random errors and the unobserved value of the covariate (latent variable) follows a Student t and skew-t distribution, respectively. The results and methods are numerically illustrated with an example in the field of dentistry.
dc.languageeng
dc.publisherSPRINGER
dc.relationStatistical Papers
dc.rightsCopyright SPRINGER
dc.rightsrestrictedAccess
dc.subjectSkew-t distribution
dc.subjectGibbs algorithm
dc.subjectMetropolis-Hasting
dc.subjectSkewness
dc.subjectMultivariate null intercepts model
dc.subjectMeasurement error
dc.titleBayesian analysis of skew-t multivariate null intercept measurement error model
dc.typeArtículos de revistas


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