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A Support Vector Method for the Deconvolution Problem
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 Title & Authors
A Support Vector Method for the Deconvolution Problem
Lee, Sung-Ho;
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 Abstract
This paper considers the problem of nonparametric deconvolution density estimation when sample observa-tions are contaminated by double exponentially distributed errors. Three different deconvolution density estima-tors are introduced: a weighted kernel density estimator, a kernel density estimator based on the support vector regression method in a RKHS, and a classical kernel density estimator. The performance of these deconvolution density estimators is compared by means of a simulation study.
 Keywords
Kernel density estimator;deconvolution;reproducing kernel Hilbert space(RKHS);support vector method;
 Language
English
 Cited by
1.
A Note on Deconvolution Estimators when Measurement Errors are Normal,;

Communications for Statistical Applications and Methods, 2012. vol.19. 4, pp.517-526 crossref(new window)
2.
A note on nonparametric density deconvolution by weighted kernel estimators,;

Journal of the Korean Data and Information Science Society, 2014. vol.25. 4, pp.951-959 crossref(new window)
1.
A Note on Deconvolution Estimators when Measurement Errors are Normal, Communications for Statistical Applications and Methods, 2012, 19, 4, 517  crossref(new windwow)
2.
A note on SVM estimators in RKHS for the deconvolution problem, Communications for Statistical Applications and Methods, 2016, 23, 1, 71  crossref(new windwow)
3.
A note on nonparametric density deconvolution by weighted kernel estimators, Journal of the Korean Data and Information Science Society, 2014, 25, 4, 951  crossref(new windwow)
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