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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Camila Borelli Zeller & Celso Rômulo Barbosa Cabral & Víctor Hugo Lachos & Luis Benites, 2019. "Finite mixture of regression models for censored data based on scale mixtures of normal distributions," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(1), pages 89-116, March.
    • Handle: RePEc:spr:advdac:v:13:y:2019:i:1:d:10.1007_s11634-018-0337-y
    DOI: 10.1007/s11634-018-0337-y
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