Bandwidth Selection for Weighted Kernel Density Estimation
Abstract
In the this paper, the authors propose to estimate the density of a targeted population with a weighted kernel density estimator (wKDE) based on a weighted sample. Bandwidth selection for wKDE is discussed. Three mean integrated squared error based bandwidth estimators are introduced and their performance is illustrated via Monte Carlo simulation. The least-squares cross-validation method and the adaptive weight kernel density estimator are also studied. The authors also consider the boundary problem for interval bounded data and apply the new method to a real data set subject to informative censoring.
- Publication:
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arXiv e-prints
- Pub Date:
- September 2007
- DOI:
- 10.48550/arXiv.0709.1616
- arXiv:
- arXiv:0709.1616
- Bibcode:
- 2007arXiv0709.1616W
- Keywords:
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- Statistics - Methodology
- E-Print:
- Will be rewritten for resubmission