Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Chain-Ladder Loss Reserving (Mack Model)× | Bootstrap Inference× | Vispārīgais mazāko kvadrātu metodes (GLS) novērtētājs× | |
|---|---|---|---|
| Nozare≠ | Aktuārā zinātne | Statistika | Statistika |
| Saime | Regression model | Regression model | Regression model |
| Izcelsmes gads≠ | 1993 | 1979 | 1935 |
| Autors≠ | Thomas Mack | Bradley Efron | Alexander Craig Aitken |
| Tips≠ | Stochastic loss reserving model | Resampling-based inference | Linear estimator |
| Pirmavots≠ | Mack, T. (1993). Distribution-free calculation of the standard error of chain ladder reserve estimates. ASTIN Bulletin, 23(2), 213–225. DOI ↗ | Efron, B. (1979). Bootstrap Methods: Another Look at the Jackknife. Annals of Statistics, 7(1), 1-26. DOI ↗ | Aitken, A. C. (1935). IV.—On least squares and linear combination of observations. Proceedings of the Royal Society of Edinburgh, 55, 42–48. DOI ↗ |
| Citi nosaukumi≠ | Development Factor Method, Link Ratio Method, Loss Development Method, Zincir Merdiven Yöntemi | bootstrap, bootstrap resampling, nonparametric bootstrap, Bootstrap Çıkarımı | GLS, Aitken estimator, EGLS, feasible GLS |
| Saistītās≠ | 3 | 5 | 3 |
| Kopsavilkums≠ | Chain-Ladder Reserving is a stochastic actuarial method for estimating outstanding claim liabilities from a run-off triangle of cumulative paid losses. Formalized by Thomas Mack in 1993, it provides distribution-free estimates of reserve amounts along with their standard errors, making it a cornerstone of property-casualty insurance reserving and regulatory practice worldwide. | 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. | Generalized Least Squares (GLS) is a linear regression estimator that extends ordinary least squares to handle situations where the error terms are correlated or have non-constant variance (heteroscedasticity). Introduced by Alexander Craig Aitken in 1935, GLS achieves the Best Linear Unbiased Estimator (BLUE) under a general error covariance structure by weighting observations according to their precision, providing a theoretical bridge between OLS and modern linear mixed models. |
| ScholarGateDatu kopa ↗ |
|
|
|