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Quantile regression neural networks: Implementation inRand application to precipitation downscaling

Authors
Journal
Computers & Geosciences
0098-3004
Publisher
Elsevier
Publication Date
Volume
37
Issue
9
Identifiers
DOI: 10.1016/j.cageo.2010.07.005
Keywords
  • Probabilistic
  • Nonlinear
  • Nonparametric
  • Artificial Neural Network
  • Prediction Interval
  • Quantile
  • Precipitation
  • Downscaling
  • R Programming Language
Disciplines
  • Computer Science

Abstract

Abstract The qrnn package for R implements the quantile regression neural network, which is an artificial neural network extension of linear quantile regression. The model formulation follows from previous work on the estimation of censored regression quantiles. The result is a nonparametric, nonlinear model suitable for making probabilistic predictions of mixed discrete-continuous variables like precipitation amounts, wind speeds, or pollutant concentrations, as well as continuous variables. A differentiable approximation to the quantile regression error function is adopted so that gradient-based optimization algorithms can be used to estimate model parameters. Weight penalty and bootstrap aggregation methods are used to avoid overfitting. For convenience, functions for quantile-based probability density, cumulative distribution, and inverse cumulative distribution functions are also provided. Package functions are demonstrated on a simple precipitation downscaling task.

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