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http://dx.doi.org/10.5351/CKSS.2010.17.3.451

A Support Vector Method for the Deconvolution Problem  

Lee, Sung-Ho (Department of Statistics, Daegu University)
Publication Information
Communications for Statistical Applications and Methods / v.17, no.3, 2010 , pp. 451-457 More about this Journal
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;
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Times Cited By KSCI : 1  (Citation Analysis)
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