Flexible regression modeling for censored data based on mixtures of student-t distributions

Autor(es)

Lachos, Victor H. ^[1] ; Cabral, Celso R. B. ^[2] ; Prates, Marcos O. ^[3] ; Dey, Dipak K. ^[1]

Afiliação do(s) autor(es)

^[1] Univ Connecticut, Dept Stat, Storrs, CT 06269 - USA

^[2] Univ Fed Amazonas, Dept Estat, Ave Gen Rodrigo Octavio 6200, Coroado 1, BR-69080900 Manaus, Amazonas - Brazil

^[3] Univ Fed Minas Gerais, Dept Estat, Belo Horizonte, MG - Brazil

• Tipo de documento: Artigo Científico
• Fonte: Computational Statistics; v. 34, n. 1, p. 123-152, MAR 2019.
• Citações Web of Science: 0

Resumo

In some applications of censored regression models, the distribution of the error terms departs significantly from normality, for instance, in the presence of heavy tails, skewness and/or atypical observation. In this paper we extend the censored linear regression model with normal errors to the case where the random errors follow a finite mixture of Student-t distributions. This approach allows us to model data with great flexibility, accommodating multimodality, heavy tails and also skewness depending on the structure of the mixture components. We develop an analytically tractable and efficient EM-type algorithm for iteratively computing maximum likelihood estimates of the parameters, with standard errors as a by-product. The algorithm has closed-form expressions at the E-step, that rely on formulas for the mean and variance of the truncated Student-t distributions. The efficacy of the method is verified through the analysis of simulated and real datasets. The proposed algorithm and methods are implemented in the new R package CensMixReg. (AU)

• Processo FAPESP: 15/20922-5 - Modelagem flexível em regressão para dados com censura
• Beneficiário: Víctor Hugo Lachos Dávila
• Modalidade de apoio: Auxílio à Pesquisa - Pesquisador Visitante - Brasil
• Processo FAPESP: 18/05013-7 - Modelos semi-paramétricos de efeitos mistos com respostas múltiplas censuradas sob a classe de distribuições misturas de escala normal
• Beneficiário: Larissa Avila Matos
• Modalidade de apoio: Auxílio à Pesquisa - Pesquisador Visitante - Internacional