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Advances in Data Analysis and Classification
Erschienen in: Advances in Data Analysis and Classification 1/2019

24.08.2018 | Regular Article

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

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

Erschienen in: Advances in Data Analysis and Classification | Ausgabe 1/2019

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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.
Vorheriger Artikel Clustering via finite nonparametric ICA mixture models
Nächster Artikel Finite mixture biclustering of discrete type multivariate data

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Metadaten
Titel
Finite mixture of regression models for censored data based on scale mixtures of normal distributions
verfasst von
Camila Borelli Zeller
Celso Rômulo Barbosa Cabral
Víctor Hugo Lachos
Luis Benites
Publikationsdatum
24.08.2018
Verlag
Springer Berlin Heidelberg
Erschienen in
Advances in Data Analysis and Classification / Ausgabe 1/2019
Print ISSN: 1862-5347
Elektronische ISSN: 1862-5355
DOI
https://doi.org/10.1007/s11634-018-0337-y

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