10.6084/m9.figshare.5661616.v1 Thalita do Bem Mattos Thalita do Bem Mattos Aldo M. Garay Aldo M. Garay Victor H. Lachos Victor H. Lachos Likelihood-based inference for censored linear regression models with scale mixtures of skew-normal distributions Taylor & Francis Group 2017 Censored regression models heavy tails SAEM algorithm scale mixtures of skew-normal distributions 2017-12-03 07:35:27 Journal contribution https://tandf.figshare.com/articles/journal_contribution/Likelihood-based_inference_for_censored_linear_regression_models_with_scale_mixtures_of_skew-normal_distributions/5661616 <p>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-<i>t</i>, 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.</p>