1. Fitting insurance and economic data with outliers: a flexible approach based on finite mixtures of contaminated gamma distributions.
- Author
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Punzo, Antonio, Mazza, Angelo, and Maruotti, Antonello
- Subjects
- *
PARAMETRIC modeling , *ECONOMIC statistics , *BIG data , *GAMMA distributions , *INSURANCE statistics - Abstract
Insurance and economic data are frequently characterized by positivity, skewness, leptokurtosis, and multi-modality; although many parametric models have been used in the literature, often these peculiarities call for more flexible approaches. Here, we propose a finite mixture of contaminated gamma distributions that provides a better characterization of data. It is placed in between parametric and non-parametric density estimation and strikes a balance between these alternatives, as a large class of densities can be implemented. We adopt a maximum likelihood approach to estimate the model parameters, providing the likelihood and the expected-maximization algorithm implemented to estimate all unknown parameters. We apply our approach to an artificial dataset and to two well-known datasets as the workers compensation data and the healthcare expenditure data taken from the medical expenditure panel survey. The Value-at-Risk is evaluated and comparisons with other benchmark models are provided. [ABSTRACT FROM AUTHOR]
- Published
- 2018
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