Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
권호 표지

Smoothing Parameter Selection in Nonparametric Spectral Density Estimation

  • Kang, Kee-Hoon (Department of Computer Science and Statistics, Seoul National University) ;
  • Park, Byeong-U (Department of Computer Science and Statistics, Seoul National University) ;
  • Cho, Sin-Sup (Department of Computer Science and Statistics, Seoul National University) ;
  • Kim, Woo-Chul (Department of Computer Science and Statistics, Seoul National University)
  • Published : 1995.12.01
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper we consider kernel type estimator of the spectral density at a point in the analysis of stationary time series data. The kernel entails choice of smoothing parameter called bandwidth. A data-based bandwidth choice is proposed, and it is obtained by solving an equation similar to Sheather(1986) which relates to the probability density estimation. A Monte Carlo study is done. It reveals that the spectral density estimates using the data-based bandwidths show comparatively good performance.

Keywords

References

  1. Journal of Time Series Analysis v.8 Determining the bandwidth of a kernel spectrum estimate Belrtao,K.I.;Bloomfield,P.
  2. Time Series Data Analysis and Theory Brillinger,D.R.
  3. Journal of American Statistical Association v.80 Data-driven choice of a spectrum estimate: Extending the applicability of cross-validation methods Hurvich,C.M.
  4. Journal of Statistical Planning and Inferences On estimating integrated squared spectral density derivatives Lee,Y.H.;Kim,W.C.;Park,B.U.
  5. Journal of Korean Statistical Society v.23 An automatic spectral density estimate Park,B.U.;Cho,S.;Kang,K.H.
  6. Spectral Analysis and Time Series Priestly,M.B.
  7. Communications in Statistics-Data Analysis v.4 An improved data-based algorithm for choosing the windows width when estimating the density at a point Sheather,S.J.
  8. Economica v.59 Autmatic frequency domain inference on semiparametric and nonparametric models Robinson,P.M.