dc.creatorGaray
dc.creatorAM; Lachos
dc.creatorVH; Lin
dc.creatorTI
dc.date2016
dc.date2016-12-06T18:32:17Z
dc.date2016-12-06T18:32:17Z
dc.date.accessioned2018-03-29T02:04:53Z
dc.date.available2018-03-29T02:04:53Z
dc.identifier1938-7997
dc.identifierStatistics And Its Interface. INT PRESS BOSTON, INC, n. 9, n. 3, p. 281 - 293.
dc.identifier1938-7989
dc.identifierWOS:000371290600003
dc.identifier10.4310/SII.2016.v9.n3.a3
dc.identifierhttp://intlpress.com/site/pub/pages/journals/items/sii/content/vols/0009/0003/a003/index.html
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/320501
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1311267
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionIn the framework of censored nonlinear regression models, the random errors are routinely assumed to have a normal distribution, mainly for mathematical convenience. However, this method has been criticized in the literature due to its sensitivity to deviations from the normality assumption. In practice, data such as income or viral load in AIDS studies, often violate this assumption because of heavy tails. In this paper, we establish a link between the censored nonlinear regression model and a recently studied class of symmetric distributions, which extends the normal one by the inclusion of kurtosis, called scale mixtures of normal (SMN) distributions. The Student-t, Pearson type VII, slash and contaminated normal, among others distributions, are contained in this class. Choosing a member of this class can be a good alternative to model this kind of data, because they have been shown its flexibility in several applications. We develop an analytically simple and efficient EM-type algorithm for iteratively computing maximum likelihood estimates of model parameters together with standard errors as a by-product. The algorithm uses nice expressions at the E-step, relying on formulae for the mean and variance of truncated SMN distributions. The usefulness of the proposed methodology is illustrated through applications to simulated and real data.
dc.description9
dc.description
dc.description281
dc.description293
dc.descriptionFundacao de Amparo a Pesquisa doEstado de Sao Paulo [2013/21468-0, 2014/02938-9]
dc.descriptionConselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq-Brazil)
dc.descriptionMinistry of Science and Technology of Taiwan [MOST 103-2118-M-005-001-MY2]
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.description
dc.description
dc.description
dc.languageEnglish
dc.publisherINT PRESS BOSTON, INC
dc.publisherSOMERVILLE
dc.relationStatistics and Its Interface
dc.rightsfechado
dc.sourceWOS
dc.subjectCensored Nonlinear Regression Model
dc.subjectEm-type Algorithms
dc.subjectScale Mixtures Of Normal Distributions
dc.subjectOutliers
dc.titleNonlinear Censored Regression Models With Heavy-tailed Distributions
dc.typeArtículos de revistas


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