A Support Vector Method for the Deconvolution Problem Lee, Sung-Ho;
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;