Spatiotemporal bias adjustment of IMERG satellite precipitation data across Canada
Recently developed remote sensing data including satellite-based products show promising
performance in estimating precipitation at high spatiotemporal resolution. However, the …
performance in estimating precipitation at high spatiotemporal resolution. However, the …
Noncrossing quantile regression curve estimation
Since quantile regression curves are estimated individually, the quantile curves can cross,
leading to an invalid distribution for the response. A simple constrained version of quantile …
leading to an invalid distribution for the response. A simple constrained version of quantile …
Non-crossing nonlinear regression quantiles by monotone composite quantile regression neural network, with application to rainfall extremes
AJ Cannon - Stochastic environmental research and risk …, 2018 - Springer
The goal of quantile regression is to estimate conditional quantiles for specified values of
quantile probability using linear or nonlinear regression equations. These estimates are …
quantile probability using linear or nonlinear regression equations. These estimates are …
Seismic amplification maps of Italy based on site-specific microzonation dataset and one-dimensional numerical approach
Prediction of surface ground motion based on advanced approaches is a non-trivial task at
large area. In fact, advanced approaches require a detailed geological and geotechnical …
large area. In fact, advanced approaches require a detailed geological and geotechnical …
Expectile and quantile regression—David and Goliath?
LS Waltrup, F Sobotka, T Kneib… - Statistical …, 2015 - journals.sagepub.com
Recent interest in modern regression modelling has focused on extending available (mean)
regression models by describing more general properties of the response distribution. An …
regression models by describing more general properties of the response distribution. An …
Learning quantile functions without quantile crossing for distribution-free time series forecasting
Quantile regression is an effective technique to quantify uncertainty, fit challenging
underlying distributions, and often provide full probabilistic predictions through joint …
underlying distributions, and often provide full probabilistic predictions through joint …
Non-crossing quantile regression for distributional reinforcement learning
Distributional reinforcement learning (DRL) estimates the distribution over future returns
instead of the mean to more efficiently capture the intrinsic uncertainty of MDPs. However …
instead of the mean to more efficiently capture the intrinsic uncertainty of MDPs. However …
Deep non-crossing probabilistic wind speed forecasting with multi-scale features
Clean and renewable wind energy has made an outstanding contribution to alleviating the
energy crisis. However, the randomness and volatility of wind brings great risk to the …
energy crisis. However, the randomness and volatility of wind brings great risk to the …
Non-crossing quantile regression neural network as a calibration tool for ensemble weather forecasts
Despite the maturity of ensemble numerical weather prediction (NWP), the resulting
forecasts are still, more often than not, under-dispersed. As such, forecast calibration tools …
forecasts are still, more often than not, under-dispersed. As such, forecast calibration tools …
A novel time series probabilistic prediction approach based on the monotone quantile regression neural network
J Hu, J Tang, Z Liu - Information Sciences, 2024 - Elsevier
Quantile regression is widely applied in various fields such as economy, energy,
meteorological prediction research in recent years since it does not require distribution …
meteorological prediction research in recent years since it does not require distribution …