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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"Bandwidth Selection for Kernel Conditional Density Estimation,"
Monash Econometrics and Business Statistics Working Papers
16/98, Monash University, Department of Econometrics and Business Statistics.
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