Prediction via the Quantile-Copula Conditional Density Estimator
To make a prediction of a response variable from an explanatory one which takes into account features such as multimodality, a nonparametric approach based on an estimate of the conditional density is advocated and considered. In particular, we build point and interval predictors based on the quantile-copula estimator of the conditional density by Faugeras . The consistency of these predictors is proved through a uniform consistency result of the conditional density estimator. Eventually, the practical implementation of these predictors is discussed. A simulation on a real data set illustrates the proposed methods.
|Date of creation:||07 Dec 2009|
|Date of revision:|
|Contact details of provider:|| Phone: (+33) 5 61 12 86 23|
Web page: http://www.tse-fr.eu/
More information through EDIRC
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Faugeras, Olivier P., 2009. "A quantile-copula approach to conditional density estimation," Journal of Multivariate Analysis, Elsevier, vol. 100(9), pages 2083-2099, October.
When requesting a correction, please mention this item's handle: RePEc:tse:wpaper:22247. See general information about how to correct material in RePEc.
If references are entirely missing, you can add them using this form.