Bootstrap and Resampling Methods

 
Semester 1 2012/13
Tuesday 14-17, Schreiber 210
http://www.tau.ac.il/ ∼ saharon/Resampling2012.html
Lecturer: Saharon Rosset
Schreiber 022
saharon@post.tau.ac.il
Office hrs: Tuesday 17-18 or by appointment.

Announcements and handouts

(23 November) Code example I showed in class of non-parametric bootstrap failure vs parametric bootstrap success on uniform problem
(1 November) Homework 1 due on 20 November in class
(20 November) Code example I showed in class of calculating confidence intervals for the spatial data in four different ways
(25 November) Homework 2 due on 11 December in class
(27 November) Code example I showed in class for testing hypothesis of unimodality
(4 December) Code example I showed in class of calibrating the smoothing parameter
(11 December) Aya's slides from her talk in class
(18 December) Code example for bagging trees on Netflix data
(19 December) Homework 3 due on 8 January in class (extended to 15 January)
(25 December) Code example for running the Metropolis algorithm on circles in a rectangle
(1 January) Code example for running the Gibbs sampler on beta-binomial example
(10 January) Homework 4 due on 29 January
(29 January) Final links: Code for problem 1, and paper for problem 2.

Syllabus

The goal of this course is to introduce the main ideas and uses of the Bootstrap and related methods.
The first part of the course will follow the book Än Introduction to the Bootstrap" by Efron and Tibshirani.
We will cover chapters 1-19 and possibly some material from later chapters.
The rest of the course will cover some of the following areas, as time and the mutual interest of instructor and students dictate:  
Tentative plan for first half+:
Week 1: Introduction, up to chapter 6
Week 2: Chap. 7-11
Week 3: Chap. 12-14
Week 4-5: Chap. 15-19
Week 6-8: Bootstrap applications from the literature
The final grade will be based on a combination of homework and a final take home exam. The homework and exam will require a combination of theoretical work and some programming and data analysis.

Prerequisites

Solid knowledge of mathematical foundations: Calculus; Linear Algebra
Undergraduate courses in: Probability; Statistical Theory; Applied Statistics (e.g., Regression)
Statistical programming experience in R is an advantage

Main textbook

An Introduction to the Bootstrap by Efron and Tibshrani (1993, Chapman and Hall). The library has two copies.

Computing

The course will require some use of statistical modeling software. It is strongly recommended to use R (freely available for PC/Unix/Mac).
R Project website also contains extensive documentation.
A basic "getting you started in R" tutorial. Uses the Boston Housing Data (thanks to Giles Hooker).
Modern Applied Statistics with Splus by Venables and Ripley is an excellent source for statistical computing help for R/Splus.



File translated from TEX by TTHgold, version 4.00.
On 29 Jan 2013, 12:10.