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

Autor(es)

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

• Tipo de documento: Artigo Científico
• Fonte: JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION; v. 90, n. 16, p. 17-pg., 2020-07-18.

Resumo

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)

• Processo FAPESP: 13/07375-0 - CeMEAI - Centro de Ciências Matemáticas Aplicadas à Indústria
• Beneficiário: Francisco Louzada Neto
• Modalidade de apoio: Auxílio à Pesquisa - Centros de Pesquisa, Inovação e Difusão - CEPIDs