Censored regression models under the class of scale mixture of skew-normal distributions

Author(s)

Monique Bettio Massuia

• Document type: Master's Dissertation
• Press: Campinas, SP.
• Institution: Universidade Estadual de Campinas (UNICAMP). Instituto de Matemática, Estatística e Computação Científica
• Defense date: 2015-06-03

Examining board members

Víctor Hugo Lachos Dávila; Filidor Edilfonso Vilca Labra; Luis Mauricio Castro Cepero

• Advisor: Víctor Hugo Lachos Dávila

Abstract

This work aims to present the linear regression model with censored response variable under the class of scale mixture of skew-normal distributions (SMSN), generalizing the well known Tobit model as providing a more robust alternative to the normal distribution. A study based on classic inference is developed to investigate these censored models under two special cases of this family of distributions, Normal and t-Student, using the EM algorithm for obtaining maximum likelihood estimates and developing methods of diagnostic based on global and local influence as suggested by Cook (1986) and Poom & Poon (1999). Under a Bayesian approach, the censored regression model was studied under some special cases of SMSN class, such as Normal, t-Student, skew-Normal, skew-t and skew-Slash. In these cases, the Gibbs sampler was the main tool used to make inference about the model parameters. We also present some simulation studies for evaluating the developed methodologies that, finally, are applied on two real data sets. The packages SMNCensReg, CensRegMod and BayesCR implemented for the software R give computational support to this work (AU)

• FAPESP's process: 12/18702-9 - Linear and non-linear models for censored data using scale mixtures of skew-normal distributions.
• Grantee: Monique Bettio Massuia
• Support Opportunities: Scholarships in Brazil - Master