Estimation of Spectral Risk Measures
Machine Learning
2019-12-24 v1 Machine Learning
Abstract
We consider the problem of estimating a spectral risk measure (SRM) from i.i.d. samples, and propose a novel method that is based on numerical integration. We show that our SRM estimate concentrates exponentially, when the underlying distribution has bounded support. Further, we also consider the case when the underlying distribution is either Gaussian or exponential, and derive a concentration bound for our estimation scheme. We validate the theoretical findings on a synthetic setup, and in a vehicular traffic routing application.
Keywords
Cite
@article{arxiv.1912.10398,
title = {Estimation of Spectral Risk Measures},
author = {Ajay Kumar Pandey and Prashanth L. A. and Sanjay P. Bhat},
journal= {arXiv preprint arXiv:1912.10398},
year = {2019}
}