Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
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A Study of Log-Fourier Deconvolution

  • Ja Yong Koo (Department of Statistics, Hallym University, Chunchon, 200-702, Korea) ;
  • Hyun Suk Park (Candidate of Ph. D., Department of Statistics, Hallym University, Chunchon, 200-702, Korea)
  • Published : 1997.12.01
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Abstract

Fourier expansion is considered for the deconvolution problem of estimating a probability density function when the sample observations are contaminated with random noise. In the log-Fourier method of density estimation for data without noise, the logarithm of the unknown density function is approximated by a trigonometric function, the unknown parameters of which are estimated by maximum likelihood. The log-Fourier density estimation method, which has been considered theoretically by Koo and Chung (1997), is studied for the finite-sample case with noise. Numerical examples using simulated data are given to show the performance of the log-Fourier deconvolution.

Keywords

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