TY - DATA T1 - Likelihood-based inference for censored linear regression models with scale mixtures of skew-normal distributions PY - 2017/12/03 AU - Thalita do Bem Mattos AU - Aldo M. Garay AU - Victor H. Lachos UR - https://tandf.figshare.com/articles/journal_contribution/Likelihood-based_inference_for_censored_linear_regression_models_with_scale_mixtures_of_skew-normal_distributions/5661616 DO - 10.6084/m9.figshare.5661616.v1 L4 - https://ndownloader.figshare.com/files/9878095 KW - Censored regression models KW - heavy tails KW - SAEM algorithm KW - scale mixtures of skew-normal distributions N2 - 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. ER -