Confidence intervals for quantiles and value-at-risk when applying importance sampling
Document Type
Conference Proceeding
Publication Date
12-1-2010
Abstract
We develop methods to construct asymptotically valid confidence intervals for quantiles and value-at-risk when applying importance sampling (IS). We first employ IS to estimate the cumulative distribution function (CDF), which we then invert to obtain a point estimate of the quantile. To construct confidence intervals, we show that the IS quantile estimator satisfies a Bahadur-Ghosh representation, which implies a central limit theorem (CLT) for the quantile estimator and can be used to obtain consistent estimators of the variance constant in the CLT. ©2010 IEEE.
Identifier
79951606193 (Scopus)
ISBN
[9781424498666]
Publication Title
Proceedings Winter Simulation Conference
External Full Text Location
https://doi.org/10.1109/WSC.2010.5678970
ISSN
08917736
First Page
2751
Last Page
2761
Recommended Citation
Chu, Fang and Nakayama, Marvin K., "Confidence intervals for quantiles and value-at-risk when applying importance sampling" (2010). Faculty Publications. 5930.
https://digitalcommons.njit.edu/fac_pubs/5930
