In practice, data often display heteroscedasticity, making quantile regression (QR) a more appropriate methodology. Modeling the data, while maintaining flexible nonparametric fitting, requires smoothing over high-dimensional space which might not be feasible when number of predictor variables is large. This problem makes necessary use dimension reduction techniques for conditional quantiles, f...

We present a neural network model for estimation of multiple conditional quantiles that satisfies the noncrossing property. Motivated by linear quantile regression, we propose with inequality constraints. In particular, to use first-order optimization method, develop new algorithm fitting proposed model. This gives nearly optimal solution without projected gradient step requires polynomial comp...

A nonparametric kernel methods is proposed and evaluated performance for estimating annual maximum stream flow quantiles. The bandwidth of the estimator is estimated by using an optimal technique and a cross-validation technique. Results obtained from a limited amount of real data from Malaysia show that quantiles estimated by nonparametric method using these techniques have small root mean squ...

BACKGROUND Quantiles are a staple of epidemiologic research: in contemporary epidemiologic practice, continuous variables are typically categorized into tertiles, quartiles and quintiles as a means to illustrate the relationship between a continuous exposure and a binary outcome. DISCUSSION In this paper we argue that this approach is highly problematic and present several potential alternati...

In order to obtain reference curves for data sets when the covariate is multidimensional, we propose a new methodology based on dimension-reduction and nonparametric estimation of conditional quantiles. This semiparametric approach combines sliced inverse regression (SIR) and a kernel estimation of conditional quantiles. The convergence of the derived estimator is shown. By a simulation study, ...

A smoothing spline is considered to propose a novel model for the time-varying quantile of the univariate time series using a state space approach. A correlation is further incorporated between the dependent variable and its one-step-ahead quantile. Using a Bayesian approach, an efficient Markov chain Monte Carlo algorithm is described where we use the multi-move sampler, which generates simult...