Journal of Hydrometeorology, Vol. 17, No. 6 (June 2016), pp. 1869-1883 (15 pages) ABSTRACT Classical regression models are widely used in hydrological regional frequency analysis (RFA) in order to ...

Although quantile regressions are widely employed for heterogeneous data, simultaneously selecting covariates that globally affect the response and estimating the coefficients is very challenging. We ...

The goal of a machine learning regression problem is to predict a single numeric value. Quantile regression is a variation where you are concerned with under-prediction or over-prediction. I'll phrase ...

One of the more difficult challenges for modeling is deciding how (or if) to deal with extreme data points. It’s a common problem in economic and financial numbers. Fat tailed distributions are ...

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This paper examines a set of value-at-risk (VaR) models and their ability to appropriately describe and capture price-change risk in the European energy market. We make in-sample, one-day-ahead VaR ...

Firms and workers, on average, are more productive in larger cities. One possible explanation which has been studied for a long time is that firms and workers in larger cities benefit from ...