Within the loss distribution approach framework, the required capital is the 99.9% value-at-risk of the annual loss distribution, which is based on the fit of the severity and frequency distributions ...

The estimation problem of the quantiles ξ + bσ of an exponential distribution with unknown location-scale parameter (ξ, σ) is considered. We establish the admissibility of the traditional (best ...

Operational risk reserves are still widely estimated using the loss distribution approach. The accuracy of the estimation depends heavily on the accuracy with which the extreme quantiles of the ...

A procedure based on order statistics is given for the interval estimation of the largest α-quantile of several continuous distributions. Results on infimum of coverage probability are obtained for ...

Random forests (RFs) have become a cornerstone for nonparametric estimation and prediction, yet their finite-sample properties—particularly in survival analysis and quantile estimation—remain ...

Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of neural network quantile regression. The goal of a quantile regression problem is to predict a single numeric ...