Nothing
SWIM: Scenario Weights for Importance Measurement
An efficient sensitivity analysis for stochastic models based on Monte Carlo samples. Provides weights on simulated scenarios from a stochastic model, such that stressed random variables fulfil given probabilistic constraints (e.g. specified values for risk measures), under the new scenario weights. Scenario weights are selected by constrained minimisation of the relative entropy to the baseline model. The 'SWIM' package is based on Pesenti S.M., Millossovich P., Tsanakas A. (2019) "Reverse Sensitivity Testing: What does it take to break the model" <openaccess.city.ac.uk/id/eprint/18896/> and Pesenti S.M. (2021) "Reverse Sensitivity Analysis for Risk Modelling" <https://www.ssrn.com/abstract=3878879>.
Getting started
Browse package contents
Package details |
|
|---|---|
| Author | Silvana M. Pesenti [aut, cre] (<https://orcid.org/0000-0002-6661-6970>), Alberto Bettini [aut], Pietro Millossovich [aut] (<https://orcid.org/0000-0001-8269-7507>), Andreas Tsanakas [aut] (<https://orcid.org/0000-0003-4552-5532>), Zhuomin Mao [ctb], Kent Wu [ctb] |
| Maintainer | Silvana M. Pesenti <swimpackage@gmail.com> |
| License | GPL-3 |
| Version | 1.0.0 |
| URL | https://github.com/spesenti/SWIM https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3515274 https://utstat.toronto.edu/pesenti/?page_id=138 |
| Package repository | View on CRAN |
| Installation |
Install the latest version of this package by entering the following in R:
|
Try the SWIM package in your browser
Any scripts or data that you put into this service are public.
