TY - JOUR
T1 - On the Contaminated Weighted Exponential Distribution
T2 - Applications to Modeling Insurance Claim Data
AU - Mahdavi, Abbas
AU - Kharazmi, Omid
AU - Contreras-Reyes, Javier E.
N1 - Publisher Copyright:
© 2022 by the authors.
PY - 2022/11
Y1 - 2022/11
N2 - 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.
AB - 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.
KW - bayesian estimation
KW - EM algorithm
KW - Gibbs sampler
KW - insurance claim data
KW - Mixture model
UR - http://www.scopus.com/inward/record.url?scp=85141785493&partnerID=8YFLogxK
U2 - 10.3390/jrfm15110500
DO - 10.3390/jrfm15110500
M3 - Article
AN - SCOPUS:85141785493
SN - 1911-8074
VL - 15
JO - Journal of Risk and Financial Management
JF - Journal of Risk and Financial Management
IS - 11
M1 - 500
ER -