Prediction via the Quantile-Copula Conditional Density Estimator
AbstractTo 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.
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Bibliographic InfoPaper provided by Toulouse School of Economics (TSE) in its series TSE Working Papers with number 09-124.
Date of creation: 07 Dec 2009
Date of revision:
nonparametric estimation; modal regressor; level-set;
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- Faugeras, Olivier P., 2009. "A quantile-copula approach to conditional density estimation," Journal of Multivariate Analysis, Elsevier, vol. 100(9), pages 2083-2099, October.
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