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

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.

Keywords:

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

Acknowledgements

The authors thank the editors and reviewers for their constructive comments on an earlier version of this manuscript.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The study was partially supported by CAPES and the Center for Mathematical Sciences Applied to Industry (CeMEAI) funded by FAPESP (grant 2013/07375-0) from the Brazilian government (M. Santos-Neto), and by FONDECYT (grant 1200525) from the National Agency for Research and Development of the Chilean government (V. Leiva).