Robust estimates of insurance misrepresentation through kernel quantile regression mixtures
提出一种基于核分位数回归混合模型的非参数方法，用于稳健估计保险数据中的虚假陈述比例并识别可疑个体，在医疗支出面板调查数据中发现显著虚假陈述。
Abstract This paper pertains to a class of nonparametric methods for studying the misrepresentation issue in insurance applications. For this purpose, mixture models based on quantile regression in reproducing kernel Hilbert spaces are employed. Compared with the existing parametric approaches, the proposed framework features a more flexible statistics structure which could alleviate the risk of model misspecification, and is in the meantime more robust to outliers in the data. The proposed framework can not only estimate the prevalence of misrepresentation in the data, but also help identify the most suspicious individuals for the validation purpose. Through embedding state‐of‐the‐art machine learning techniques, we present a novel statistics procedure to efficiently estimate the proposed misrepresentation model in the presence of massive data. The proposed methodology is applied to study the Medical Expenditure Panel Survey data, and a significant degree of misrepresentation activity is found on the self‐reported insurance status.