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

Author(s)

Desousa, Mario F. ; Saulo, Helton ; Santos-Neto, Manoel ; Leiva, Victor

• Document type: Journal article
• Source: JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION; v. 90, n. 16, p. 17-pg., 2020-07-18.

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. (AU)

• FAPESP's process: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry
• Grantee: Francisco Louzada Neto
• Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC