Beta kernel estimators for density functions
Computational Statistics & Data Analysis
Bandwidth selection for kernel conditional density estimation
Computational Statistics & Data Analysis
Goodness-of-fit tests for copulas
Journal of Multivariate Analysis
Nonparametric Estimation of Conditional Distributions
IEEE Transactions on Information Theory
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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.