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>