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Nonparametric analysis of aggregate loss models
| Content Provider | Semantic Scholar |
|---|---|
| Author | Vilar, Juan Manuel Cao, Ricardo Ausín, María Concepción González-Fragueiro, C. |
| Copyright Year | 2007 |
| Abstract | This paper describes a nonparametric approach to make inference for aggregate loss models in the insurance framework. We assume that an insurance company provides a historical sample of claims given by claim occurence times and claim sizes. Furthermore, information may be incomplete as claims may be censored and/or truncated. In this context, the main goal of this work consists in fitting a probability model for the total amount that will be paid on all claims during a fixed future time period. In order to solve this prediction problem, we propose a new methodology based on nonparametric estimators for the density functions with censored and truncated data, the use of Monte Carlo simulation methods and bootstrap resampling. The developed methodology is useful to obtain the best strategy in different insurance decision problems. The proposed procedure is illustrated with a real data set provided by the insurance department of an international commercial company. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://citeseer.ist.psu.edu/viewdoc/download;jsessionid=397B0683BABD52886E359E144F87A595?doi=10.1.1.575.3616&rep=rep1&type=pdf |
| Alternate Webpage(s) | http://citeseer.ist.psu.edu/viewdoc/download;jsessionid=BE196CEF37438865919575CD49060023?doi=10.1.1.575.3616&rep=rep1&type=pdf |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Aggregate data Censor Decision problem Hearing Loss, High-Frequency Inference Leucaena pulverulenta Monte Carlo method Monte Carlo methods for option pricing Resampling (statistics) Simulation Truncation (statistics) |
| Content Type | Text |
| Resource Type | Article |
