STAT 546: Nonparametric Models in Statistics
This course is offered once every two years.
Time: 12:10pm MWF at PHYSICS 0058.
NO LECTURE IN THE FISRT WEEK.
First lecture is on Monday, September 2nd (We will have a late start on Week 2 due to attending the 59th World Congress in Statistisc at HK)
We will decide on the time (which I do not like) at the first meet.
Course Outline: The course is focused
on smoothing techniques for estimating density, regression and other functional
curves without a parametric family of models. Smoothing methods, together
with the Bootstrap and perhaps the Empirical Likelihood are the key members of modern
nonparametric statistical methods. Thanks to the availability of modern
computational and graphical tools and the increases in the amount of data at
our disposal, it has become feasible to move away from the classical parametric
models. When we are using nonparametric methods in exploratory data
analysis or model building and inference, we are "letting data to speak
for themselves." In the course, we will
discuss how to set up a nonparametric model, how to choose the amount of
smoothing, and how to evaluate the resulting fits.
The method we will cover extensively is the Kernel Method. I have been working on nonparametric curve estimation for many years, and will share the experience with the students. The course will consists of projects where students have the opportunity to practice nonparametric estimation techniques based on their own data of interest. The objective is to enable students to understand how and why these techniques work and know how to implement them in practice.
Credits: 3 Pre-requisites: Stat 511 and Stat 542 or consent of instructor.
Assessement: 40% from one project and 20% from Assignments, 40% Final Exam.
Simonoff, J. S. (1996). Smoothing Methods in Statistics. Springer.
Silverman, B. (1986). Density Estimation for Statistics and Data Analysis. Chapman and Hall.
Hardle, W. (1990). Applied Nonparametric Regression. Cambridge University Press.
Fan, J. and I. Gijbels (1996) Local Polynomial Smoothing. Chapman and Hall.