We develop a new quantile autoregression neural network (QARNN) model based on an
artificial neural network architecture. The proposed QARNN model is flexible and can be …

The aim of this paper is to present a functional analysis of a diffusion tensor tract statistics
(FADTTS) pipeline for delineating the association between multiple diffusion properties …

This paper develops a quantile hidden semi-Markov regression to jointly estimate multiple
quantiles for the analysis of multivariate time series. The approach is based upon the …

We propose a new support vector weighted quantile regression approach that is closely built
upon the idea of support vector machine. We extend the methodology of several popular …

The increase in the frequency and intensity of hazardous hydrometeorological phenomena
is one of the most dangerous consequences of climate instability. In this study, we …

Multichannel sensor systems are widely used in condition monitoring for effective failure
prevention of critical equipment or processes. However, loss of sensor readings due to …

We study evaluating a policy under best-and worst-case perturbations to a Markov decision
process (MDP), given transition observations from the original MDP, whether under the …

This article deals with the conditional density estimation when the explanatory variable is
functional. In fact, nonparametric kernel type estimator of the conditional density has been …

This paper studies the asymptotic properties of the nonlinear quantile regression model
under general assumptions on the error process, which is allowed to be heterogeneous and …

In the estimation of a dose–response curve, parametric models are straightforward and
efficient but subject to model misspecifications; nonparametric methods are robust but less …