Likelihood-based inference for censored linear regression models with scale mixtures of skew-normal distributions

Autor(es)

Mattos, Thalita do Bem ^[1] ; Garay, Aldo M. ^[2] ; Lachos, Victor H. ^[3]

Afiliação do(s) autor(es)

^[1] Univ Estadual Campinas, Dept Stat, Campinas, SP - Brazil

^[2] Univ Fed Pernambuco, Dept Stat, Recife, PE - Brazil

^[3] Univ Connecticut, Dept Stat, Storrs, CT 06269 - USA

• Tipo de documento: Artigo Científico
• Fonte: Journal of Applied Statistics; v. 45, n. 11, p. 2039-2066, 2018.
• Citações Web of Science: 1

Resumo

In many studies, the data collected are subject to some upper and lower detection limits. Hence, the responses are either left or right censored. A complication arises when these continuous measures present heavy tails and asymmetrical behavior; simultaneously. For such data structures, we propose a robust-censored linear model based on the scale mixtures of skew-normal (SMSN) distributions. The SMSN is an attractive class of asymmetrical heavy-tailed densities that includes the skew-normal, skew-t, skew-slash, skew-contaminated normal and the entire family of scale mixtures of normal (SMN) distributions as special cases. We propose a fast estimation procedure to obtain the maximum likelihood (ML) estimates of the parameters, using a stochastic approximation of the EM (SAEM) algorithm. This approach allows us to estimate the parameters of interest easily and quickly, obtaining as a byproducts the standard errors, predictions of unobservable values of the response and the log-likelihood function. The proposed methods are illustrated through real data applications and several simulation studies. (AU)

• Processo FAPESP: 13/21468-0 - Modelos com erros nas variáveis para dados censurados usando distribuições de misturas da escala skew-normal
• Beneficiário: Aldo William Medina Garay
• Modalidade de apoio: Bolsas no Brasil - Pós-Doutorado