Võrdle meetodeid
Vaata valitud meetodeid kõrvuti; erinevad read on esile tõstetud.
| Olulisuse valim (Importance Sampling)× | Äärmusväärtuste teooria (EVT)× | |
|---|---|---|
| Valdkond≠ | Simulatsioon | Rahandus |
| Perekond≠ | Process / pipeline | Regression model |
| Tekkeaasta≠ | 1951 | 2001 |
| Looja≠ | Herman Kahn & Theodore Harris (RAND Corporation, 1951) | Coles (textbook treatment); McNeil, Frey & Embrechts |
| Tüüp≠ | Monte Carlo variance-reduction technique | Tail / extreme-event model |
| Algallikas≠ | Rubinstein, R.Y. & Kroese, D.P. (2016). Simulation and the Monte Carlo Method (3rd ed.). Wiley. DOI ↗ | Coles, S. (2001). An Introduction to Statistical Modeling of Extreme Values. Springer. ISBN: 978-1852334598 |
| Rööpnimetused≠ | IS, weighted Monte Carlo, Önem Örneklemesi | EVT, generalized extreme value, generalized Pareto distribution, peaks over threshold |
| Seotud | 5 | 5 |
| Kokkuvõte≠ | Importance sampling is a Monte Carlo variance-reduction technique that shifts the sampling distribution toward the region of interest — typically a rare or extreme event — so that informative samples are drawn far more often than under the original distribution. Developed at the RAND Corporation by Herman Kahn and Theodore Harris around 1951, it makes tail-probability estimation (such as Value-at-Risk or system-failure probability) tractable where standard Monte Carlo would require an astronomically large number of runs. | Extreme Value Theory is a statistical framework for modelling the rare events that live in the tail of a probability distribution. As developed in Coles (2001) and applied to risk by McNeil, Frey & Embrechts (2005), it offers two standard routes: the Generalized Extreme Value (GEV) distribution for block maxima and the Generalized Pareto Distribution (GPD), used in the peaks-over-threshold approach, for exceedances above a high threshold. |
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