방법 비교
선택한 방법을 나란히 검토하세요. 서로 다른 행은 강조 표시됩니다.
| 중요도 샘플링× | 극단값 이론 (Extreme Value Theory, EVT)× | |
|---|---|---|
| 분야≠ | 시뮬레이션 | 재무학 |
| 계열≠ | Process / pipeline | Regression model |
| 기원 연도≠ | 1951 | 2001 |
| 창시자≠ | Herman Kahn & Theodore Harris (RAND Corporation, 1951) | Coles (textbook treatment); McNeil, Frey & Embrechts |
| 유형≠ | Monte Carlo variance-reduction technique | Tail / extreme-event model |
| 원전≠ | 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 |
| 별칭≠ | IS, weighted Monte Carlo, Önem Örneklemesi | EVT, generalized extreme value, generalized Pareto distribution, peaks over threshold |
| 관련 | 5 | 5 |
| 요약≠ | 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. |
| ScholarGate데이터셋 ↗ |
|
|