Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Резервування збитків методом «ланцюгової драбини» (модель Мака)× | Узагальнений метод найменших квадратів (УНМК)× | |
|---|---|---|
| Галузь≠ | Актуарна наука | Статистика |
| Родина | Regression model | Regression model |
| Рік появи≠ | 1993 | 1935 |
| Автор методу≠ | Thomas Mack | Alexander Craig Aitken |
| Тип≠ | Stochastic loss reserving model | Linear estimator |
| Основоположне джерело≠ | Mack, T. (1993). Distribution-free calculation of the standard error of chain ladder reserve estimates. ASTIN Bulletin, 23(2), 213–225. 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 ↗ |
| Інші назви≠ | Development Factor Method, Link Ratio Method, Loss Development Method, Zincir Merdiven Yöntemi | GLS, Aitken estimator, EGLS, feasible GLS |
| Пов'язані | 3 | 3 |
| Підсумок≠ | 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. | 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. |
| ScholarGateНабір даних ↗ |
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