For spline regressions, it is well known that the choice of knots is crucial for the performance
of the estimator. As a general learning framework covering the smoothing splines, learning …

Recently developed remote sensing data including satellite-based products show promising
performance in estimating precipitation at high spatiotemporal resolution. However, the …

Classical regression methods have focused mainly on estimating conditional mean
functions. In recent years, however, quantile regression has emerged as a comprehensive …

Recent interest in modern regression modelling has focused on extending available (mean)
regression models by describing more general properties of the response distribution. An …

In this article we consider quantile regression in reproducing kernel Hilbert spaces, which
we call kernel quantile regression (KQR). We make three contributions:(1) we propose an …

Supplement to “Estimation in functional linear quantile regression”. This supplementary file
contains the additional discussion on the connection to nonlinear ill-posed inverse …

In recent years, there has been growing interest in neural network to explore complex
patterns. We consider an extension of this framework in composite quantile regression setup …

Quantiles are computed by optimizing an asymmetrically weighted L1 norm, ie the sum of
absolute values of residuals. Expectiles are obtained in a similar way when using an L2 …

We discuss a practical and effective framework to estimate reference growth charts via
regression quantiles. Inequality constraints are used to ensure both monotonicity and non …

With the growing wind penetration of wind resources into power generation worldwide,
accurate and comprehensive wind speed forecasting (WSF) is becoming increasingly …