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.

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

R code

Cite

@article{HY02,
  author  = {Rob J Hyndman and Qiwei Yao},
  doi     = {10.1080/10485250212374},
  journal = {J Nonparametric Statistics},
  number  = {3},
  pages   = {259--278},
  title   = {Nonparametric estimation and symmetry tests for conditional density functions},
  volume  = {14},
  year    = 2002
}