| EN, publication_article_article_name |
Bayesian Bonus-Malus Premium with Poisson-Lindley Distributed Claim Frequency and Lognormal-Gamma Distributed Claim Severity in Automobile Insurance |
| EN, publication_article_accepted_date |
7 September 2020 |
| EN, publication_article_journal |
| EN, publication_article_journal_name |
WSEAS Transactions on Mathematics |
| EN, publication_article_journal_standard |
SCOPUS |
| EN, publication_article_institute |
World Scientific and Engineering Academy and Society |
| EN, publication_article_isbn |
2224-2880 |
| EN, publication_article_year |
|
| EN, publication_article_issue |
|
| EN, publication_article_month |
|
| EN, publication_article_print_year |
2021 |
| EN, publication_article_page |
443-451 |
| EN, publication_article_abstract |
The traditional automobile insurance bonus-malus system (BMS) merit-rating depends on the number of claims. An insured individual who makes a small severity claim is penalized unfairly compared to an insured person who makes a large severity claim. A model for assigning the bonus-malus premium was
proposed. Consideration was based on both the number and size of the claims that were assumed to follow a Poisson-Lindley distribution and a Lognormal-Gamma distribution, respectively. The Bayesian method was applied to compute the bonus-malus premiums,
integrated by both frequency and severity components based on
the posterior criteria. Practical examples using a real data set are provided. This approach offers a fairer method of penalizing all policyholders in the portfolio. |
| EN, publication_article_keyword |
Automobile insurance, Bayesian method, Bonus-malus system, Claim severity, Number of claims, Poisson-Lindley distribution, Lognormal-Gamma distribution |
| EN, publication_article_writer |
|
| EN, publication_article_evaluation |
มีผู้ประเมินอิสระ |
| EN, publication_article_status |
ตีพิมพ์แล้ว |
| EN, publication_article_level |
นานาชาติ |
| EN, publication_article_citation |
EN, publication_article_citation_false |
| EN, publication_article_part_of_thesis |
EN, publication_article_part_of_thesis_true |
| EN, publication_article_part_of_graduate |
EN, publication_article_part_of_graduate_false |
| EN, publication_attachment_file |
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| Citation |
0
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EN, publication_detail
EN, publication_article
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