On a new mixture-based regression model: simulation and application to data with high censoring

Mário F. Desousa, Helton Saulo, Manoel Santos-Neto, VICTOR ELISEO LEIVA SANCHEZ

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

In this paper, we derive a new continuous-discrete mixture regression model which is useful for describing highly censored data. This mixture model employs the Birnbaum-Saunders distribution for the continuous response variable of interest, whereas the Bernoulli distribution is used for the point mass of the censoring observations. We estimate the corresponding parameters with the maximum likelihood method. Numerical evaluation of the model is performed by means of Monte Carlo simulations and of an illustration with real data. The results show the good performance of the proposed model, making it an addition to the tool-kit of biometricians, medical doctors, applied statisticians, and data scientists.

Original languageEnglish
Pages (from-to)1-17
Number of pages17
JournalJournal of Statistical Computation and Simulation
DOIs
StatePublished - 2020
Externally publishedYes

Keywords

  • Bernoulli and Birnbaum-Saunders distributions
  • censoring
  • maximum likelihood method
  • mixture distributions
  • Monte Carlo simulation
  • R software

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