Finite mixture of regression models for censored data based on scale mixtures of normal distributions

Camila Borelli Zeller, Celso Rômulo Barbosa Cabral, Víctor Hugo Lachos, Luis Benites

Research output: Contribution to journalArticle in a journalpeer-review

26 Scopus citations

Abstract

In statistical analysis, particularly in econometrics, the finite mixture of regression models based on the normality assumption is routinely used to analyze censored data. In this work, an extension of this model is proposed by considering scale mixtures of normal distributions (SMN). This approach allows us to model data with great flexibility, accommodating multimodality and heavy tails at the same time. The main virtue of considering the finite mixture of regression models for censored data under the SMN class is that this class of models has a nice hierarchical representation which allows easy implementation of inferences. We develop a simple EM-type algorithm to perform maximum likelihood inference of the parameters in the proposed model. To examine the performance of the proposed method, we present some simulation studies and analyze a real dataset. The proposed algorithm and methods are implemented in the new R package CensMixReg.

Original languageEnglish
Pages (from-to)89-116
Number of pages28
JournalAdvances in Data Analysis and Classification
Volume13
Issue number1
DOIs
StatePublished - 8 Mar 2019
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2018, Springer-Verlag GmbH Germany, part of Springer Nature.

Keywords

  • Censoring
  • EM-type algorithm
  • Finite mixture of regression models
  • Scale mixtures of normal distributions

Fingerprint

Dive into the research topics of 'Finite mixture of regression models for censored data based on scale mixtures of normal distributions'. Together they form a unique fingerprint.

Cite this