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A quantile-copula approach to conditional density estimation

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Author Info
Faugeras, Olivier P.
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.

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Publisher Info
Article provided by Elsevier in its journal Journal of Multivariate Analysis.

Volume (Year): 100 (2009)
Issue (Month): 9 (October)
Pages: 2083-2099
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Handle: RePEc:eee:jmvana:v:100:y:2009:i:9:p:2083-2099

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Related research
Keywords: Copula Conditional density Kernel estimation Nonparametric regression Quantile transform;

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This page was last updated on 2009-12-30.

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