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

Thalita do Bem Mattos, Aldo M. Garay and Victor H. Lachos

Journal of Applied Statistics, 2018, vol. 45, issue 11, 2039-2066

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.

Date: 2018
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Citations: View citations in EconPapers (5)

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DOI: 10.1080/02664763.2017.1408788

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