Week 1 Introduction

• Nonparametric Regresion
• Chapter 1, pages 1-11 in Eubank.
  
• Measuring error in regression problems
• Chapter 1, pages 11-17 and Chapter 2, pages 27-37 in Eubank.
  

Week 2 Orthogonal Series Estimators

• Polynomial bases
  
• Chapter 2 pp. 37-40 and Chapter 3 pp. 119-127 in Eubank.
  
• Fourier bases
• Chapter 3 pp. 74-78 and pp. 85-103 in Eubank.
  
• The performance of the unbiased estimator of P is described in Eubank Chapter 2.3 pp. 50-62.
• There is a brief description of  shrinkage estimators in Wasserman Chapter 7.6 pp. 155-158.
  

Week 3 Fitting Orthogonal Series

• Pratice fitting Fourier bases and Efromovich's universal estimator (R code)
  
• Handout: Efromovich (1999) Nonparametric Curve Estimation pp. 118-126 and 59-63.
• Here are some updated functions to do a better job of  calculating the coefficients for the cosine basis functions
  
• Fitting trigonometric series with nonuniform design
  

Week 4 Wavelet Estimators

• Wavelet bases
• Wavelet threshold estimators
• Here is the code I used in class using R's wavelets package.
  

Week 5 Kernel Estimators

• Kernel estimators
• Eubank,  Chapters 4.1-4.3 pp. 155-169.
  
• Boundary kernels
• Eubank,  Chapter 4.3-4.4 pp. 169-178.
  

Week 6 Local Linear Smoothers

• Nadaraya-Watson estimator and local linear regression
• Eubank, Chapters 4.7 pp. 189-194,
• Wasserman, All of Nonparametric  Statistics, Chapter 5.4 on pp. 71-80.
• Hastie, Tibshirani, and Friedman, The Elements of Statistical Learning, Chapter 6.1 on pp.165-172.
  
• Cross validation techniques for estimating bandwidth
  

Week 7  Spline Estimators

• Regression splines
• Eubank, Chapter 6.
  
• Natural splines and smoothing splines
• Eubank, Chapter 5.1 and 5.8
  

Week 8   Smoothing Splines

• Smoothing Splines
• Eubank Chapter 5.2, 5.3 and 5.4
• "Smoothing Spline-A Tutorial" by Brian Yandell
  

Week 9 

• Generalized regression models
• Confidence intervals
  

Week 10

• Bayesian nonparametric regression
• Semiparametric regression