On the Contaminated Weighted Exponential Distribution: Applications to Modeling Insurance Claim Data

Abbas Mahdavi, Omid Kharazmi and Javier E. Contreras-Reyes ()
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Abbas Mahdavi: Department of Statistics, Vali-e-Asr University of Rafsanjan, Rafsanjan 7718897111, Iran
Omid Kharazmi: Department of Statistics, Vali-e-Asr University of Rafsanjan, Rafsanjan 7718897111, Iran
Javier E. Contreras-Reyes: Instituto de Estadística, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso 2360102, Chile

JRFM, 2022, vol. 15, issue 11, 1-18

Abstract: Deriving loss distribution from insurance data is a challenging task, as loss distribution is strongly skewed with heavy tails with some levels of outliers. This paper extends the weighted exponential (WE) family to the contaminated WE (CWE) family, which offers many flexible features, including bimodality and a wide range of skewness and kurtosis. We adopt Expectation-Maximization (EM) and Bayesian approaches to estimate the model, providing the likelihood and the priors for all unknown parameters. Finally, two sets of claims data are analyzed to illustrate the efficiency of the proposed method in detecting outliers.

Keywords: bayesian estimation; EM algorithm; Gibbs sampler; Mixture model; insurance claim data (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2022
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