qrnn-package {qrnn} | R Documentation |
This package implements the quantile regression neural network (QRNN) (Taylor, 2000), which is the artificial neural network analog of linear quantile regression. The QRNN formulation follows from previous work on the estimation of censored regression quantiles, thus allowing predictions for mixed discrete-continuous variables like precipitation (Friederichs and Hense, 2007). A differentiable approximation to the quantile regression cost function is adopted so that a simplified form of the finite smoothing algorithm (Chen, 2007) can be used to estimate model parameters. Quantile-based distribution functions are provided following Quinonero-Candela (2006).
Package: | qrnn |
Type: | Package |
License: | GPL-2 |
Alex J. Cannon <http://www.eos.ubc.ca/~acannon>
Cannon, A.J., 2011. Quantile regression neural networks: implementation in R and application to precipitation downscaling, Computers & Geosciences, 37: 1277-1284. doi:10.1016/j.cageo.2010.07.005
Google Scholar citationsChen, C., 2007. A finite smoothing algorithm for quantile regression. Journal of Computational and Graphical Statistics, 16: 136-164.
Friederichs, P. and A. Hense, 2007. Statistical downscaling of extreme precipitation events using censored quantile regression. Monthly Weather Review, 135: 2365-2378.
Quinonero-Candela, J., C. Rasmussen, F. Sinz, O. Bousquet, B. Scholkopf, 2006. Evaluating Predictive Uncertainty Challenge. Lecture Notes in Artificial Intelligence, 3944: 1-27.
Taylor, J.W., 2000. A quantile regression neural network approach to estimating the conditional density of multiperiod returns. Journal of Forecasting, 19(4): 299-311.
https://cran.r-project.org/package=qrnn