A multivariate claim count model for applications in insurance
- 作者： Selch, Daniela Anna, author.
- 其他作者：
- 其他題名：
- Springer actuarial.
- 出版： Cham : Springer International Publishing :Imprint: Springer
- 叢書名： Springer actuarial,
- 主題： Multivariate analysis. , Insurance claims. , Probability Theory and Stochastic Processes. , Actuarial Sciences. , Statistics for Business/Economics/Mathematical Finance/Insurance. , Statistical Theory and Methods. , Quantitative Finance.
- ISBN： 9783319928685 (electronic bk.) 、 9783319928678 (paper)
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FIND@SFXID:
CGU
- 資料類型: 電子書
- 內容註: 1 Motivation and Model -- 2 Properties of the Model -- 3 Estimation of the Parameters -- 4 Applications and Extensions -- 5 Appendix: Technical Background -- References -- Index.
- 摘要註: This monograph presents a time-dynamic model for multivariate claim counts in actuarial applications. Inspired by real-world claim arrivals, the model balances interesting stylized facts (such as dependence across the components, over-dispersion and the clustering of claims) with a high level of mathematical tractability (including estimation, sampling and convergence results for large portfolios) and can thus be applied in various contexts (such as risk management and pricing of (re-)insurance contracts) The authors provide a detailed analysis of the proposed probabilistic model, discussing its relation to the existing literature, its statistical properties, different estimation strategies as well as possible applications and extensions. Actuaries and researchers working in risk management and premium pricing will find this book particularly interesting. Graduate-level probability theory, stochastic analysis and statistics are required.
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讀者標籤：
- 系統號： 005437220 | 機讀編目格式
館藏資訊
This monograph presents a time-dynamic model for multivariate claim counts in actuarial applications. Inspired by real-world claim arrivals, the model balances interesting stylized facts (such as dependence across the components, over-dispersion and the clustering of claims) with a high level of mathematical tractability (including estimation, sampling and convergence results for large portfolios) and can thus be applied in various contexts (such as risk management and pricing of (re-)insurance contracts). The authors provide a detailed analysis of the proposed probabilistic model, discussing its relation to the existing literature, its statistical properties, different estimation strategies as well as possible applications and extensions. Actuaries and researchers working in risk management and premium pricing will find this book particularly interesting. Graduate-level probability theory, stochastic analysis and statistics are required.