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A Support Vector Method for the Deconvolution Problem

Lee, Sung-Ho

  • Received : 20100200
  • Accepted : 20100300
  • Published : 2010.05.31

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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  2. A Note on Deconvolution Estimators when Measurement Errors are Normal vol.19, pp.4, 2012, https://doi.org/10.5351/CKSS.2012.19.4.517
  3. A note on nonparametric density deconvolution by weighted kernel estimators vol.25, pp.4, 2014, https://doi.org/10.7465/jkdi.2014.25.4.951