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http://dx.doi.org/10.5351/CSAM.2016.23.1.071

A note on SVM estimators in RKHS for the deconvolution problem  

Lee, Sungho (Department of Statistics, Daegu University)
Publication Information
Communications for Statistical Applications and Methods / v.23, no.1, 2016 , pp. 71-83 More about this Journal
Abstract
In this paper we discuss a deconvolution density estimator obtained using the support vector machines (SVM) and Tikhonov's regularization method solving ill-posed problems in reproducing kernel Hilbert space (RKHS). A remarkable property of SVM is that the SVM leads to sparse solutions, but the support vector deconvolution density estimator does not preserve sparsity as well as we expected. Thus, in section 3, we propose another support vector deconvolution estimator (method II) which leads to a very sparse solution. The performance of the deconvolution density estimators based on the support vector method is compared with the classical kernel deconvolution density estimator for important cases of Gaussian and Laplacian measurement error by means of a simulation study. In the case of Gaussian error, the proposed support vector deconvolution estimator shows the same performance as the classical kernel deconvolution density estimator.
Keywords
deconvolution; ill-posed problem; kernel density estimator; regularization; reproducing kernel Hilbert space (RKHS); support vector machines (SVM);
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Times Cited By KSCI : 2  (Citation Analysis)
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