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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.)

Flexible regression modeling for censored data based on mixtures of student-t distributions

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Lachos, Victor H. [1] ; Cabral, Celso R. B. [2] ; Prates, Marcos O. [3] ; Dey, Dipak K. [1]
Total Authors: 4
[1] Univ Connecticut, Dept Stat, Storrs, CT 06269 - USA
[2] Univ Fed Amazonas, Dept Estat, Ave Gen Rodrigo Octavio 6200, Coroado 1, BR-69080900 Manaus, Amazonas - Brazil
[3] Univ Fed Minas Gerais, Dept Estat, Belo Horizonte, MG - Brazil
Total Affiliations: 3
Document type: Journal article
Source: Computational Statistics; v. 34, n. 1, p. 123-152, MAR 2019.
Web of Science Citations: 0

In some applications of censored regression models, the distribution of the error terms departs significantly from normality, for instance, in the presence of heavy tails, skewness and/or atypical observation. In this paper we extend the censored linear regression model with normal errors to the case where the random errors follow a finite mixture of Student-t distributions. This approach allows us to model data with great flexibility, accommodating multimodality, heavy tails and also skewness depending on the structure of the mixture components. We develop an analytically tractable and efficient EM-type algorithm for iteratively computing maximum likelihood estimates of the parameters, with standard errors as a by-product. The algorithm has closed-form expressions at the E-step, that rely on formulas for the mean and variance of the truncated Student-t distributions. The efficacy of the method is verified through the analysis of simulated and real datasets. The proposed algorithm and methods are implemented in the new R package CensMixReg. (AU)

FAPESP's process: 15/20922-5 - Flexible regression modeling for censored data
Grantee:Víctor Hugo Lachos Dávila
Support type: Research Grants - Visiting Researcher Grant - Brazil
FAPESP's process: 18/05013-7 - Semiparametric mixed effects models with multiple censored response using scale mixtures of normal distributions
Grantee:Larissa Avila Matos
Support type: Research Grants - Visiting Researcher Grant - International