Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Бутстреп-інференс× | Узагальнений метод найменших квадратів (УНМК)× | Модель розподілу збитків× | |
|---|---|---|---|
| Галузь≠ | Статистика | Статистика | Актуарна наука |
| Родина | Regression model | Regression model | Regression model |
| Рік появи≠ | 1979 | 1935 | 2012 |
| Автор методу≠ | Bradley Efron | Alexander Craig Aitken | Klugman, Panjer & Willmot |
| Тип≠ | Resampling-based inference | Linear estimator | Parametric probability model |
| Основоположне джерело≠ | 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 ↗ | 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 |
| Інші назви≠ | bootstrap, bootstrap resampling, nonparametric bootstrap, Bootstrap Çıkarımı | GLS, Aitken estimator, EGLS, feasible GLS | Severity-Frequency Model, Aggregate Loss Model, Claim Size Distribution Model, Hasar Dağılımı Modeli |
| Пов'язані≠ | 5 | 3 | 3 |
| Підсумок≠ | 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. | 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. |
| ScholarGateНабір даних ↗ |
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