Composite Weibull-Inverse Transformed Gamma distribution and its actuarial application

Maghsoudi, Mastoureh; Bakar, Shaiful Anuar Abu; Hamzah, Nor Aishah

doi:10.1063/1.4887728

Cited by 2 publications

(1 citation statement)

References 6 publications

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“…Nadarajah and Bakar [8] developed the composite Lognormal-Burr model. Maghsoudi et al [9] concluded that among composite Weibull-Gamma family, composite Weibull-Inverse Transformed Gamma model is better than other models. Composite models based on Stoppa distribution were developed by Calderin-Ojeda and Kwok [10].…”

Section: Introductionmentioning

confidence: 99%

Some Composite Models and Their Application to Turkish Motor Insurance Data

Yiğiter

Gençtürk

Hamurkaroğlu

2019

Eskişehir Teknik Üniversitesi Bilim Ve Teknoloji Dergisi B - Teorik Bilimler

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It is important for an insurance company to predict the future claims in order to evaluate premiums, to determine the reserve necessary to meet its obligation and probabilities of ruin, etc. as the claim data is highly positively skewed and has heavy tail, no standard parametric model seems to provide an acceptable fit to both small and large losses. Composite models that use one standard distribution up to a threshold and other standard distribution thereafter are developed and it is seen that these composite models provide better fit than the standard models when claim data involve small and high claims. The aim of this study is to investigate the use of the composite models namely Exponential-Pareto, Weibull-Pareto and Lognormal-Pareto to model the Turkish Motor Insurance claim data. From the results obtained, it is concluded that the composite Weibull-Pareto model provides better fit to Turkish Motor Insurance claim data than the all other models considered.

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Section: Introductionmentioning

confidence: 99%

Some Composite Models and Their Application to Turkish Motor Insurance Data

Yiğiter

Gençtürk

Hamurkaroğlu

2019

Eskişehir Teknik Üniversitesi Bilim Ve Teknoloji Dergisi B - Teorik Bilimler

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Danish fire insurance data: A review and additional analysis

Shongwe,

Marambakuyana

2024

ITM Web Conf.

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The Danish fire insurance data is one of the most recognised and well-known datasets in the empirical insurance claims literature. This dataset is used in many textbooks and articles to illustrate the analysis of fire insurance claims, more specifically in the application of heavy-tailed loss distributions and extreme value theory. In this paper, we provide a short review of publications that used the Danish fire insurance data and conduct an additional analysis. Our additional analysis on the Danish fire insurance data involves investigating the: (i) modality issue using appropriate statistical tests and software, (ii) k-means clustering pattern using different techniques, (iii) effect of using a splicing model on the data, and (iv) differences in results that we obtained as compared to what other cited researchers reported in their earlier publications. In short, the objective of this paper is to highlight the importance of the Danish fire claims dataset by showcasing different models where it has been used to verify certain hypotheses in the empirical actuarial field. Additional analyses are also conducted to illustrate its dense usefulness in the actuarial and extremes field, where real-life datasets are scarce because they are often subjected to a lot of proprietary and privacy laws.

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Composite Weibull-Inverse Transformed Gamma distribution and its actuarial application

Cited by 2 publications

References 6 publications

Some Composite Models and Their Application to Turkish Motor Insurance Data

Some Composite Models and Their Application to Turkish Motor Insurance Data

Danish fire insurance data: A review and additional analysis

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