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AI Summary & Key Takeaways
AI Insight⚡ TL;DR(Too Long; Didn't Read)
Also, we discuss the consistency of the credibility premium generated by this distribution-free approach.
📌 3 Key Takeaways
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Objective & Hypothesis:This paper focuses on weighted balanced loss function under the Esscher principle (WBLF) of which we explore the modern practice of credibility theory and we generalize credibility premiums by using the WBLF.
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Key Findings & Data:We obtain a distribution-free approach under the WBLF and the Esscher premium by using a minimization technique.
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Conclusion & Impact:Also, we discuss the consistency of the credibility premium generated by this distribution-free approach.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Credibility premium · Esscher premium · loss function
Abstract
This paper focuses on weighted balanced loss function under the Esscher principle (WBLF) of which we explore the modern practice of credibility theory and we generalize credibility premiums by using the WBLF. We obtain a distribution-free approach under the WBLF and the Esscher premium by using a minimization technique. Also, we discuss the consistency of the credibility premium generated by this distribution-free approach.
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Cite This Article
METİRİ, F., ZEGHDOUDİ, H., REMİTA, M. R. (2022). On weighted balanced loss function under the Esscher principle and credibility premiums. Hacettepe Journal of Mathematics and Statistics, Cilt: 47 - Sayı: 1, 255–265. https://ajindex.com/articles/529804