一般保险损失计算的广义线性模型

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Accounts of Chemical Research Pub Date : 2022-09-07 DOI:10.18187/pjsor.v18i3.3676

D. Lestari, Raymond Tanujaya, Rahmat Al Kafi, S. Devila

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引用次数: 0

摘要

在大多数情况下，非人寿保险的损失是根据索赔的严重程度和频率以及独立性假设计算的。然而，在某些情况下，索赔的严重程度取决于索赔频率。在消除了独立性假设的情况下，本文通过对索赔严重程度和频率建模，推导了总损失计算。作者使用索赔频率作为协变量对平均索赔严重程度进行建模，以诱导它们之间的相关性。为此，我们使用广义线性模型。在进行参数估计后，我们将获得计算出的损耗。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Generalized Linear Models for Loss Calculation in General Insurance

In most cases, loss in non-life insurance is calculated based on claim severity and frequency and an assumption of independence. However, in some cases, claim severity depends upon the claim frequency. This paper presents the derivation of aggregate loss calculation by modeling claim severity and frequency as the assumption of independence is eliminated. The authors modeled average claim severity using claim frequency as the covariate to induce the dependence among them. For that purpose, we use the generalized linear model. After doing parameters estimation, we will obtain the calculated loss.

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来源期刊

Accounts of Chemical Research 化学-化学综合

CiteScore

31.40

自引率

1.10%

发文量

312

审稿时长

2 months

期刊介绍： Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.