Nonparametric estimation and symmetry tests for conditional density functions


Rob J Hyndman, Qiwei Yao


16 July 2002

Publication details

Journal of Nonparametric Statistics 14(3), 259-278




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.

Keywords: bandwidth selection; bootstrap; conditioning; density estimation; kernel smoothing; symmetry tests.

R code