A quantile-copula approach to conditional density estimation

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 compar...

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Veröffentlicht in:	Journal of multivariate analysis 2009-10, Vol.100 (9), p.2083-2099
1. Verfasser:	Faugeras, Olivier P.
Format:	Artikel
Sprache:	eng
Schlagworte:	Bias Comparative analysis Conditional density Copula Copula Conditional density Kernel estimation Nonparametric regression Quantile transform Estimating techniques Exact sciences and technology Kernel estimation Linear inference, regression Mathematics Multivariate analysis Nonparametric inference Nonparametric regression Probability and statistics Quantile transform Regression analysis Sciences and techniques of general use Simulation Statistics Studies
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Beschreibung
Zusammenfassung:	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.
ISSN:	0047-259X 1095-7243
DOI:	10.1016/j.jmva.2009.03.006