We propose exponentially weighted quantile regression (EWQR) for estimating time-varying quantiles. The EWQR cost function can be used as the basis for estimating the time-varying expected shortfall associated with the EWQR quantile forecast. We express EWQR in a kernel estimation framework, and then modify it by adapting a previously proposed double kernel estimator in order to provide greater...

The cpquantile of an ordered sequence of data values is the element with rank ‘pn, where n is the total number of values. Accurate estimates of quantiles are required for the solution of many practical problems. In this paper, we present a new algorithm for estimating the quantile values for disk-resident data. Our algorithm has the following characteristics: (1) It requires only one pass over ...

We introduce Quantile Boost (QBoost) algorithms which predict conditional quantiles of the interested response for regression and binary classification. Quantile Boost Regression (QBR) performs gradient descent in functional space to minimize the objective function used by quantile regression (QReg). In the classification scenario, the class label is defined via a hidden variable, and the quant...

Steady state simulation is used to study long-run behavior. Usually only the expected value of the steady state probability distribution function is estimated. In many cases quantiles of this distribution are of higher interest. In this paper a new usage of quantile estimators is proposed, which is derived from mean value analysis and is based on multiple independent replications. The advantage...

A test statistic based on population quantiles using sample order statistics is suggested. The quantiles of the test statistics are evaluated for generalized exponential distribution. Similar test statistic based on moments of sample order statistic is referred and the proposed test formula is compared with it. Between the pairs of the above models it is established that the test formula propos...

We review the peaks over thresholds or POT method for modelling tails of loss severity distributions and discuss the use of this technique for estimating high quantiles and the possible relevance of this to excess of loss insurance in high layers. We test the method on a variety of simulated heavy-tailed distributions to show what kind of thresholds are required and what sample sizes are necess...

This paper studies robustness of bootstrap inference methods under moment conditions. In particular, we compare the uniform weight and implied probability bootstraps by analyzing behaviors of the bootstrap quantiles when outliers take arbitrarily large values, and derive the breakdown points for those bootstrap quantiles. The breakdown point properties characterize the situation where the impli...