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| Inferencia Bootstrap× | Model de Distribució de Pèrdues× | |
|---|---|---|
| Camp≠ | Estadística | Ciència actuarial |
| Família | Regression model | Regression model |
| Any d'origen≠ | 1979 | 2012 |
| Autor original≠ | Bradley Efron | Klugman, Panjer & Willmot |
| Tipus≠ | Resampling-based inference | Parametric probability model |
| Font seminal≠ | Efron, B. (1979). Bootstrap Methods: Another Look at the Jackknife. Annals of Statistics, 7(1), 1-26. DOI ↗ | Klugman, S. A., Panjer, H. H., & Willmot, G. E. (2012). Loss Models: From Data to Decisions (4th ed.). Wiley. ISBN: 978-1-118-31532-3 |
| Àlies | bootstrap, bootstrap resampling, nonparametric bootstrap, Bootstrap Çıkarımı | Severity-Frequency Model, Aggregate Loss Model, Claim Size Distribution Model, Hasar Dağılımı Modeli |
| Relacionats≠ | 5 | 3 |
| Resum≠ | Bootstrap inference, introduced by Bradley Efron in 1979, estimates the sampling distribution of a statistic by repeatedly resampling the observed data with replacement. It requires no distributional assumption and produces reliable confidence intervals even in small samples. | A Loss Distribution Model is a parametric statistical framework used in actuarial science to characterise the probabilistic behaviour of insurance claim amounts and frequencies. Developed comprehensively by Klugman, Panjer, and Willmot in their foundational text Loss Models: From Data to Decisions (first edition 1998, fourth edition 2012), these models underpin premium rating, reserving, reinsurance pricing, and regulatory capital calculations across the insurance and risk-management industries. |
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