Statistics Program

Spring 2004 Course

Nonparametric regression models

  • Course number : 221 U0600
  • Credits : 3 units
  • Required or Requisite : Requisite
  • Instructor : Ming-Yen Cheng
  • Time : Sat 234
  • Room : NM405
  • Notice :
  • Prerequisites :
  • Course Description :
    Kernel density estimation; local likelihood estimation; spline-based methods; kernel regression; local polynomial regression; bandwidth selection; local varying bandwidth; higher order kernels; optimal kernels; bias reduction techniques; local likelihood approaches; multiple predictors; additive models.
  • 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.
    6. Loader, C. (1999) Local regression and likelihood. Springer.
  • Grading :
    Homework(30%), Midterm(30%), Final exam(40%).

Last updated: February 26, 2004

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