Nonparametric estimation and symmetry tests for conditional density functions
AbstractAbstract: 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.
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Bibliographic InfoPaper provided by London School of Economics and Political Science, LSE Library in its series LSE Research Online Documents on Economics with number 6092.
Date of creation: 2002
Date of revision:
Publication status: Published in Journal of Nonparametric Statistics, 2002, 14(3), pp. 259-278. ISSN: 1048-5252
bandwidth selection; bootstrap; conditioning; density estimation; kernel smoothing; symmetry tests.;
Other versions of this item:
- Hyndman, R.J. & Yao, Q., 1998. "Nonparametric Estimation and Symmetry Tests for Conditional Density Functions," Monash Econometrics and Business Statistics Working Papers 17/98, Monash University, Department of Econometrics and Business Statistics.
- C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
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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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