23º SINAPE - Simpósio Nacional de Probabilidade e Estatística

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Título

INFERENCE AND DIAGNOSTICS IN LINEAR CENSORED REGRESSION MODELS WITH SKEW SCALE MIXTURES OF NORMAL DISTRIBUTIONS

Resumo

In the framework of censored regression models the random errors are routinely assumed to have a
normal distribution, mainly for mathematical convenience. However, this method has been criticized
in the literature because of its sensitivity to deviations from the normality assumption. Here, we first
establish a new link between the censored regression model and the class of assymmetric distributions
studied by Ferreira et al (2015). Skew scale mixtures of normal distributions are often used for statistical
procedures involving asymmetric data and heavy-tailed. The main virtue of the members of this
family of distributions is that they are easy to simulate from and they also supply genuine expectationmaximization
(EM) algorithms for maximum likelihood estimation. In this work, we extend the EM
algorithm for linear censored regression models and we develop diagnostics analyses via global and
local influence. The EM-type algorithm has been discussed with an emphasis on the skew Student-tnormal,
skew slash, skew-contaminated normal and skew power-exponential distributions. Finally, to
examine the performance of the proposed model, case-deletion and local influence techniques are developed
to show its robust aspect against outlying and influential observations. The proposed methods
are verified through the analysis of several simulation studies and applying in real datasets.

Palavras-chave

Censored regression model; Heavy tails; Skew scale mixtures of normal distributions; EMtype
algorithm; Case-deletion model; Local influence.

Área

Modelos de Regressão

Autores

Daniel Camilo Fuentes Guzman, Camila Borelli Zeller, Clécio da Silva Ferreira