The Bivariate Kumaraswamy Weibull regression model: a complete classical and Bayesian analysis

Abstract

Bivariate distributions play a fundamental role in survival and reliability studies. We consider a regression model for bivariate survival times under right-censored based on the bivariate Kumaraswamy Weibull (Cordeiro et al., Journal of the Franklin Institute, 347, 1399-1429, 2010) distribution to model the dependence of bivariate survival data. We describe some structural properties of the marginal distributions. The method of maximum likelihood and a Bayesian procedure are adopted to estimate the model parameters. We use diagnostic measures based on the local influence and Bayesian case influence diagnostics to detect influential observations in the new model. We also show that the estimates in the bivariate Kumaraswamy Weibull regression model are robust to deal with the presence of outliers in the data. In addition, we use some measures of goodness-of-fit to evaluate the bivariate Kumaraswamy Weibull regression model. The methodology is illustrated by means of a real lifetime data set for kidney patients.

Survival analysis of cancer patients using a new extended Weibull distribution

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The Kumaraswamy Lindley Regression Model with Application on the Egyptian Stock Exchange

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Um estudo comparativo de novos modelos de regressão paramétricos para dados de sobrevivência

“…A distribuição Kumaraswamy proposta em Kumaraswamy (1980) possui sua função densidade de probabilidade duplamente limitada nos extremos inferiores e superiores, uma característica bastante semelhante em relação à distribuição Beta. Vários modelos de regressão foram introduzidos com base nessa distribuição como De Santana et al (2012) (modelo de sobrevivência com capacidade de modelar função de taxa de falha não monótona) e Fachini-Gomes et al (2018) (apresenta uma abordagem para distribuição bivariada), porém, devido à quantidade limitada de modelos para estudo, essas distribuições não foram selecionadas.…”