Journal of Nonparametric Statistics (2002), 14(3), 259-278.
Rob J Hyndman1 and Qiwei Yao2
- Department of Econometrics and Business Statistics, Monash University, Clayton VIC 3800, Australia.
- Department of Statistics, London School of Economics Houghton Street, London WC2A 2AE, U.K.
Abstract: 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.