Please use this identifier to cite or link to this item:
DC FieldValue
dc.titleNonparametric density deconvolution by weighted kernel estimators
dc.contributor.authorHazelton, M.L.
dc.contributor.authorTurlach, B.A.
dc.identifier.citationHazelton, M.L., Turlach, B.A. (2009-09). Nonparametric density deconvolution by weighted kernel estimators. Statistics and Computing 19 (3) : 217-228. ScholarBank@NUS Repository.
dc.description.abstractNonparametric density estimation in the presence of measurement error is considered. The usual kernel deconvolution estimator seeks to account for the contamination in the data by employing a modified kernel. In this paper a new approach based on a weighted kernel density estimator is proposed. Theoretical motivation is provided by the existence of a weight vector that perfectly counteracts the bias in density estimation without generating an excessive increase in variance. In practice a data driven method of weight selection is required. Our strategy is to minimize the discrepancy between a standard kernel estimate from the contaminated data on the one hand, and the convolution of the weighted deconvolution estimate with the measurement error density on the other hand. We consider a direct implementation of this approach, in which the weights are optimized subject to sum and non-negativity constraints, and a regularized version in which the objective function includes a ridge-type penalty. Numerical tests suggest that the weighted kernel estimation can lead to tangible improvements in performance over the usual kernel deconvolution estimator. Furthermore, weighted kernel estimates are free from the problem of negative estimation in the tails that can occur when using modified kernels. The weighted kernel approach generalizes to the case of multivariate deconvolution density estimation in a very straightforward manner. © 2008 Springer Science+Business Media, LLC.
dc.subjectDensity estimation
dc.subjectErrors in variables
dc.subjectIntegrated square error
dc.subjectMeasurement error
dc.contributor.departmentSTATISTICS & APPLIED PROBABILITY
dc.description.sourcetitleStatistics and Computing
Appears in Collections:Staff Publications

Show simple item record
Files in This Item:
There are no files associated with this item.

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.