Likelihood-based inference for censored linear regression models with scale mixtures of skew-normal distributions. Issue 11 (18th August 2018)

• Record Type: Journal Article
• Authors: Mattos, Thalita do Bem; Garay, Aldo M.; Lachos, Victor H.
• Is Part Of: Journal of applied statistics. Volume 45:Issue 11(2018)
• Page Start: 2039
• Page End: 2066
• Publication Date: 2018-08-18
• Subjects: Censored regression models -- heavy tails -- SAEM algorithm -- scale mixtures of skew-normal distributions; Statistics -- Periodicals; 519.5
• DOI: 10.1080/02664763.2017.1408788
• Languages: English
• ISSNs: 0266-4763

Abstract

ABSTRACT: 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.