A Support Vector Method for the Deconvolution Problem

Lee, Sung-Ho

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


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.


Kernel density estimator;deconvolution;reproducing kernel Hilbert space(RKHS);support vector method


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