STA 293.1 Modern Nonparametric Methods
Announcements
- We'll meet two times in a week. See the new time below (1/12)
Class General Info
- Instructor: Woncheol Jang
- Phone: (919) 684-3437, email:wjang@stat.duke.edu
- Time: WF 10:05-11:20AM
- Place: Old Chem 025
- Course Website: http://www.stat.duke.edu/~wjang/teaching/S05-293/
- Textbook: Wasserman, L.A. (2005). All of Nonparametric
Statistics, Springer.
- I assume you know: Linear algebra and statistical principles at
the level of STA 213 or BGT200. You should be comfortable with the
topics: distribution fuctions, convergence in probability, convergence in distribution,
almost sure convergence, likelihood functions, confidence intervals,
the delta method, bias, mean square error.Students uncertain about
preparation are encouraged to
contact the
instructor.
- It will be useful to learn one of the following programming
languages: R (recommended), S-Plus, or MATLAB.
- Course description: Teaches modern,
computationally-based methods for exploring and drawing inferences
from data. The course covers resampling methods,
nonparametric density estimation, nonpaprametric regression and
classfication. Specifically covers: bootstrap, Kernel methods, splines,
local regression, orthogonal series estimators, Minimax theory,
Wavelets, VC Theory, support vector machines.
Class Schedule (subject to change):
- Introduction [Handout]
- Statistical Functionals [Handout]
- Resampling Methods [Handout]
- Smoothing [Handout]
- Nonparametric Regression [Handout]
- Density Estimation [Handout]
- Minimax Theory [Handout]
- Orthogonal Function Methods [Handout]
- Adaptive Methods [Handout]
- Other Topics [Handout]
Homework/Data:
Recommended Books
- Davison, A.C. and Hinkley, D.V. (1997).
Bootstrap Methods and their Application, Cambridge University Press
- Devroye, L., Györfi, L. and Lugosi, G.
(1996).
A Probabilistic Theory of Pattern Recognition. Springer.
- Efromovich, S. (1999). Nonparametric Curve
Estimation: Methods, Theory, and Applications, Springer.
- Fan, J. and Gijbels, I. (1996) Local Polynomial
Modelling and Its Applications, Chapman and Hall.
- Green, P.J. and Silverman, B.W. (1994).
Nonparametric Regression and Generalized Linear Models: A roughness
penalty approach, Chapman and
Hall.
- Hastie, T. Tibshirani, R. and Friedman, J. (2001). The Elements
of Statistical Learning: Data Mining, Inference, and Prediction,
Springer.
- Loader, C. (1999). Local Regression and
likelihood, Springer.
- Silverman, B.W. (1986) Density Estimation for
Statistics and Data Analysis, Chapman and Hall.
- van der Vaart, A.W. (1998). Asymptotic
Statistics, Cambridge University Press.
- Vapnik, V.N. (1998). Statistical Learning Theory,
Wiley.
- Vidakovic, B. (1999) Statistical Modeling by
Wavelets, Wiley
- Wahba, G. (1990) Spline Models for Observational
Data, SIAM
Computing
Other Nonparametric Courses
Last updated: 2/15/2005