1. k-sample problems, k>2
STT 430/530, Nonparametric Statistics TR 9:30-10:45 BR 201D Exam: Tuesday December 11, am
Text:
Introduction to Modern Nonparametric Statistics, J.J. Higgins, Duxbury Press (2004) (required)
We’ll proceed in a logical way to look at parametric vs. nonparametric methods for:
one-sample problems
two-sample problems
k-sample problems, k>2
Standard nonparametric techniques include rank-based methods, permutation tests, and others…
But we’ll also consider two more modern techniques: the bootstrap and smoothing methods for density estimation.
Most of these methods are highly computationally intensive - and we’ll be using the open source programming language called R to do our computations (no previous experience required)

2. For some computations we’ll also use SAS
We won’t do much theory (i.e., mathematical statistics) but won’t shy away from the notation, logic, outlines of proofs, etc., when needed
Emphasis on doing the analyses and interpreting the results - writing is important
There will be homework problems, a midterm exam and a comprehensive final exam -- the exams will involve take-home components. I’ll probably weight the three parts equally to determine your course grade.
For next Tuesday: Read Chapter 0 in the Higgins book - we’ll review some ideas from your earlier statistics courses before jumping into nonparametrics.