example I showed in class of non-parametric bootstrap failure vs
parametric bootstrap success on uniform problem
1 due on 20 November in class
example I showed in class of calculating confidence intervals for
the spatial data in four different ways
2 due on 11 December in class
example I showed in class for testing hypothesis of unimodality
example I showed in class of calibrating the smoothing parameter
slides from her talk in class
example for bagging trees on Netflix data
3 due on 8 January in class (extended to 15 January)
example for running the Metropolis algorithm on circles in a rectangle
example for running the Gibbs sampler on beta-binomial example
4 due on 29 January
(29 January) Final links:Code for problem 1, and
paper for problem 2.
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
The rest of the course will cover some of the following areas, as
time and the mutual interest of instructor and students dictate:
Applications of Bootstrap in various scientific areas: Biology
and Genetics, Economics, etc.
Advanced theoretical topics around the bootstrap: confidence
interval methodologies like BCa and ABC; Little and tiny
Other randomization-based algorithms in statistics, in
particular Markov Chain Monte Carlo (MCMC) and its applications.
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
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
An Introduction to the Bootstrap by Efron and Tibshrani (1993,
Chapman and Hall). The library has two copies.