Data-analytic approaches to regression problems, arising from many scientific disciplines
are described in this book. The aim of these nonparametric methods is to relax assumptions …

This article reviews two leading measures of financial risk and an emerging alternative.
Embraced by the Basel accords, value-at-risk and expected shortfall are the leading …

The idea for this book came from the time the authors spent at the Statistics and Applied
Mathematical Sciences Institute (SAMSI) in Research Triangle Park in North Carolina …

Amongmanyexcitingdevelopmentsinstatistic…, nonlineartimeseriesanddata-
analyticnonparametricmethodshavegreatly advanced along seemingly unrelated paths. In …

The goal of quantile regression is to estimate conditional quantiles for specified values of
quantile probability using linear or nonlinear regression equations. These estimates are …

Expectile models are derived using asymmetric least squares. A simple formula has been
presented that relates the expectile to the expectation of exceedances beyond the expectile …

In order to challenge the existing literature that points to the detachment of Bitcoin from the
global financial system, we use daily data from August 17, 2011–February 14, 2020 and …

To perform variable selection in expectile regression, we introduce the elastic-net penalty
into expectile regression and propose an elastic-net penalized expectile regression (ER-EN) …

We use tail expectiles to estimate alternative measures to the value at risk and marginal
expected shortfall, which are two instruments of risk protection of utmost importance in …

In this paper we propose a downside risk measure, the expectile-based Value at Risk
(EVaR), which is more sensitive to the magnitude of extreme losses than the conventional …