Estimation of Density via Local Polynomial Tegression

  • Park, B. U. (Department of Statistics, Seoul National University, Seoul 151-742) ;
  • Kim, W. C. (Department of Statistics, Seoul National University, Seoul 151-742) ;
  • J. Huh (Department of Statistics, Seoul National University, Seoul 151-742) ;
  • J. W. Jeon (Department of Statistics, Seoul National University, Seoul 151-742)
  • Published : 1998.03.01

Abstract

A method of estimating probability density using regression tools is presented here. It is based on equal-length binning and locally weighted approximate likelihood for bin counts. The method is particularly useful for densities with bounded supports, where it automatically corrects edge effects without using boundary kernels.

Keywords

References

  1. Journal of the Royal Statistical Society, Series A v.143 Density estimation and suicide risks in psychiatric treatment. COPAS, J.B.;FRYER, M.J.
  2. The Annals of Statistics v.21 Local linear regression smoothers and their minimax efficiencies. FAN, J.
  3. The Annals of Statistics v.20 Variable bandwidth and local linear regression smoothers. FAN, J.;GIJBELS, I.
  4. Journal of the American Statistical Association v.90 Local polynomial kernel regression for generalized linear models and quasi-likelihood functions. FAN, J.;HECKMAN, N.E.;WAND, M.P.
  5. Biometrika, to appear. A note on design transformation and binning in nonparametric curve estimation. HALL, P.;PARK, B.U.;TURLACH, B.
  6. Statistics and Computing v.3 Simple boundary correction for kernel density estimation. JONES, M.C.
  7. Biometrika v.78 Smooth optimum kernel estimators near endpoints. MULLER, H.-G.
  8. Density Estimation for Statistics and Data Analysis SILVERMAN, B.W.