Instructor:
Harrison H. Zhou
Email:
huibin.zhou@yale.edu
Class Time: Thursday 4:00-6:00PM in Room 107, 24 Hillhouse Ave (Statistics department building)
Course Description: Introduction to nonparametric methods such
as kernel estimation, Fourier basis estimation, wavelet estimation. Optimal minimax
convergence rates and constants for function spaces, with connections to
information theory. Adaptive estimators (e.g.,
adaptive shrinkage estimation). If time permits: high dimensional
function estimation, functional data estimation, classification, or
nonparametric asymptotic equivalence. Applications to real
data. Some knowledge of statistical theory at the level of STAT 610a is
assumed.
Textbook:
"Function estimation and Gaussian sequence model" by Iain
Johnstone.
Other References:
Introduction
to Nonparametric Estimation by Alexandre Tsybakov (online version available)
"All of
nonparametric statistics", by Larry Wasserman.
Syllabus:
Grade:
To pass this
course, you need to give a presentation and attend at least 10 classes.
Draft Lectures (Many mistakes are expected!):
Go to classes*v2. All draft lectures are in Resources. Many mistakes are expected!