Nonparametric Estimation and Symmetry Tests for Conditional Density Functions
We suggest two new methods for conditional density estimation. The first is based on locally fitting a log-linear model, and is in the spirit of recent work on locally parametric techniques in density estimation. The second method is a constrained local polynomial estimator. Both methods always produce non-negative estimators. We propose an algorithm suitable for selecting the two bandwidths for either estimator. We also develop a new bootstrap test for the symmetry of conditional density functions. The proposed methods are illustrated by both simulation and application to a real data set.
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- Bashtannyk, David M. & Hyndman, Rob J., 2001.
"Bandwidth selection for kernel conditional density estimation,"
Computational Statistics & Data Analysis,
Elsevier, vol. 36(3), pages 279-298, May.
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