We propose to smooth the objective function, rather than only the indicator on the check
function, in a linear quantile regression context. Not only does the resulting smoothed …

Since carbon price volatility is critical to the risk management of the CO2 emissions trading
market, research has focused on energy prices and macroeconomic drivers which cause …

Deep learning has enjoyed tremendous success in a variety of applications but its
application to quantile regression remains scarce. A major advantage of the deep learning …

The practical importance of inference with robustness against large bandwidths for causal
effects in regression discontinuity and kink designs is widely recognized. Existing robust …

Expectile-based value-at-risk (EVaR) is a more sensitive measure of the magnitude of
extreme losses compared to the conventional quantile-based value-at-risk (VaR). Besides …

Using and extending fractional order statistic theory, we characterize the O (n− 1) coverage
probability error of the previously proposed (Hutson, 1999) confidence intervals for …

Allowing for the existence of irrelevant covariates, we study the problem of estimating a
conditional quantile function nonparametrically with mixed discrete and continuous data. We …

We provide novel, high‐order accurate methods for non‐parametric inference on quantile
differences between two populations in both unconditional and conditional settings. These …

We consider inference based on local estimating equations in the presence of nuisance
parameters. The framework is useful for a number of applications including those in …

Mixture Model of Spline Truncated and Kernel in Multivariable Nonparametric Regression Page 1
Mixture Model of Spline Truncated and Kernel in Multivariable Nonparametric Regression …