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.

中文翻译：

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一种新的基于混合的回归模型：高删失数据的模拟和应用

在本文中，我们推导出一种新的连续离散混合回归模型，该模型可用于描述高度删失的数据。该混合模型将 Birnbaum-Saunders 分布用于感兴趣的连续响应变量，而 Bernoulli 分布用于审查观察的点质量。我们用最大似然法估计相应的参数。模型的数值评估是通过蒙特卡罗模拟和具有真实数据的插图进行的。结果表明所提出模型的良好性能，使其成为生物统计学家、医生、应用统计学家和数据科学家工具包的补充。