English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Expected Shortfall (ES), also known as superquantile or ConditionalValue-at-Risk, has been recognized as an important measure in risk analysis andstochastic optimization, and is also finding applications beyond these areas.In finance, it refers to the conditional expected return of an asset given thatthe return is below some quantile of its distribution. In this paper, weconsider a recently proposed joint regression framework that simultaneouslymodels the quantile and the ES of a response variable given a set ofcovariates, for which the state-of-the-art approach is based on minimizing ajoint loss function that is non-differentiable and non-convex. This inevitablyraises numerical challenges and limits its applicability for analyzinglarge-scale data. Motivated by the idea of using Neyman-orthogonal scores toreduce sensitivity with respect to nuisance parameters, we propose astatistically robust (to highly skewed and heavy-tailed data) andcomputationally efficient two-step procedure for fitting joint quantile and ESregression models. With increasing covariate dimensions, we establish explicitnon-asymptotic bounds on estimation and Gaussian approximation errors, whichlay the foundation for statistical inference. Finally, we demonstrate throughnumerical experiments and two data applications that our approach well balancesrobustness, statistical, and numerical efficiencies for expected shortfallregression.

Robust Estimation and Inference for Expected Shortfall Regression with Many Regressors