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 () and Luis Benites ()
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Camila Borelli Zeller: Universidade Federal de Juiz de Fora
Celso Rômulo Barbosa Cabral: Universidade Federal do Amazonas
Víctor Hugo Lachos: University of Connecticut
Luis Benites: Pontificia Universidad Católica del Perú

Advances in Data Analysis and Classification, 2019, vol. 13, issue 1, No 5, 89-116

Abstract: 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.

Keywords: Censoring; EM-type algorithm; Finite mixture of regression models; Scale mixtures of normal distributions; 62H30; 62J05; 62N01 (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (5)

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DOI: 10.1007/s11634-018-0337-y

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