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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Partially linear censored regression models using heavy-tailed distributions: A Bayesian approach

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Author(s):
Castro, Luis M. [1] ; Lachos, Victor H. [2] ; Ferreira, Guillermo P. [1] ; Arellano-Valle, Reinaldo B. [3]
Total Authors: 4
Affiliation:
[1] Univ Concepcion, Fac Ciencias Fis & Matemat, Dept Estadist, Concepcion - Chile
[2] Univ Estadual Campinas, IMECC, Dept Estat, Campinas, SP - Brazil
[3] Pontificia Univ Catolica Chile, Fac Matemat, Dept Estadist, Santiago 22 - Chile
Total Affiliations: 3
Document type: Journal article
Source: STATISTICAL METHODOLOGY; v. 18, p. 14-31, MAY 2014.
Web of Science Citations: 7
Abstract

Linear regression models where the response variable is censored are often considered in statistical analysis. A parametric relationship between the response variable and covariates and normality of random errors are assumptions typically considered in modeling censored responses. In this context, the aim of this paper is to extend the normal censored regression model by considering on one hand that the response variable is linearly dependent on some covariates whereas its relation to other variables is characterized by nonparametric functions, and on the other hand that error terms of the regression model belong to a class of symmetric heavy-tailed distributions capable of accommodating outliers and/or influential observations in a better way than the normal distribution. We achieve a fully Bayesian inference using pth-degree spline smooth functions to approximate the nonparametric functions. The likelihood function is utilized to compute not only some Bayesian model selection measures but also to develop Bayesian case-deletion influence diagnostics based on the q-divergence measures. The newly developed procedures are illustrated with an application and simulated data. (C) 2013 Elsevier B.V. All rights reserved. (AU)

FAPESP's process: 11/17400-6 - Applications of the scale mixture of Skew-Normal distributions in linear mixed effects models
Grantee:Víctor Hugo Lachos Dávila
Support type: Regular Research Grants
FAPESP's process: 12/19445-0 - Flexible modeling of complex longitudinal data using skew-elliptical distributions
Grantee:Víctor Hugo Lachos Dávila
Support type: Research Grants - Visiting Researcher Grant - International