| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 1146528 | Journal of Multivariate Analysis | 2009 | 17 Pages |
Abstract
A new kernel-type estimator of the conditional density is proposed. It is based on an efficient quantile transformation of the data. The proposed estimator, which is based on the copula representation, turns out to have a remarkable product form. Its large-sample properties are considered and comparisons in terms of bias and variance are made with competitors based on nonparametric regression. A comparative simulation study is also provided.
Related Topics
Physical Sciences and Engineering
Mathematics
Numerical Analysis
Authors
Olivier P. Faugeras,