Nonparametric estimation and symmetry tests for conditional density functions
We suggest two improved methods for conditional density estimation. The rst is based on locally tting 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.
|Date of creation:||2002|
|Publication status:||Published in Journal of Nonparametric Statistics, 2002, 14(3), pp. 259-278. ISSN: 1048-5252|
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