Statistics Program

Course Description

Nonparametric Regression Model

  • Course number : 201 38100
  • Credits : 3 units
  • Required or Requisite : Requisite
  • Time : Spring
  • Prerequisites : Regression Analysis, Advanced Statistical Inference
  • Course Description :
    Kernel density estimation; optimal kernels; higher order kernels; bias reduction techniques; bandwidth selection; local varying bandwidth; kernel regression; local polynomial regression; local likelihood estimation; spline-based methods; multiple predictors and additive models; smoothing ordered categorical data; goodness-of-fit tests; smoothing-based parametric estimation.
  • Textbook :
    Simonoff, J. (1996) Smoothing Methods in Statistics. Springer.
  • Reference :
  1. Silverman, B.W. (1986) Density Estimation for Statistics and Data Analysis. Chapman & Hall.
  2. Härdle,W. (1991) Applied Nonparametric Regression. Cambridge University Press.
  3. Wand, M.P. and Jones, M.C. (1995) Kernel Smoothing. Chapman & Hall.
  4. Fan, J. and Gijbel, I. (1996) Local Polynomial Modeling and its Applications. Chapman & Hall.
  5. Hart, J.D. (1997) Nonparametric Smoothing and Lack-of-Fit Tests. Springer.

Last updated: August 6, 2003

Address: Department of Mathematics, National Taiwan University, Taipei, Taiwan
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