In this paper, a new flexible approach to modeling data with multiple partial right-censoring points is proposed. This method is based on finite mixture models, flexible tool to model heterogeneity in data. A general framework to accommodate partial censoring is considered. In this setting, it is assumed that a certain portion of data points are censored and the rest are not. This situation occurs in many insurance loss data sets. A novel probability function is proposed to be used as a mixture component and the expectation-maximization algorithm is employed for estimating model parameters. The Bayesian information criterion is used for model selection. Additionally, an approach for the variability assessment of parameter estimates as well as the computation of quantiles commonly known as risk measures is considered. The proposed model is evaluated using a simulation study based on four common probability distribution functions used to model right skewed loss data and applied to a real data set with good results.

中文翻译：

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具有多个部分右删失级别的数据的混合建模

本文提出了一种具有多个局部右删失点的数据建模的灵活方法。该方法基于有限的混合模型，该模型是建模数据异质性的灵活工具。考虑了适应部分审查的一般框架。在此设置中，假定对数据点的特定部分进行检查，而对其余部分则不进行检查。在许多保险损失数据集中都会出现这种情况。提出了一种新颖的概率函数作为混合分量，并采用期望最大化算法估计模型参数。贝叶斯信息准则用于模型选择。另外，考虑了一种用于参数估计的可变性评估以及计算分位数的方法，通常称为风险度量。