Estimating the Claim Severity Distribution using Variable Neighborhood Search
- 1 Graduate School of Applied Statistic, National Institute of Development Administration, Bangkok, Thailand
In this study, Variable Neighborhood Search (VNS) is utilized to estimate the parameters of actual motor insurance claims data set and compared them obtained by the Moment Estimation Methods (MOM) and Maximum likelihood Estimation Method (MLE) which are known as a conscientious method. Then, the Kolmogorov-Smirnov test (K-S) is used to show how well the selected distribution fits the actual claims. From the results, we found that the lognormal distribution which their parameters were estimated from VNS technique fits the actual motor claims data set better than the other two techniques with significant level 0.01.
Copyright: © 2016 Kunjira Kingphai and Samruam Chongcharoen. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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- Variable Neighborhood Search (VNS)
- Claim Severity Distribution
- Maximum Likelihood Estimation Method (MLE)
- Moment Estimation Method (MOM)