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. |

(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)

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:

- Applications of Bootstrap in various scientific areas: Biology and Genetics, Economics, etc.
- Advanced theoretical topics around the bootstrap: confidence
interval methodologies like BC
_{a}and ABC; Little and tiny bootstrap; etc. - Other randomization-based algorithms in statistics, in particular Markov Chain Monte Carlo (MCMC) and its applications.

Tentative plan for first half+:

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.

Undergraduate courses in: Probability; Statistical Theory; Applied Statistics (e.g., Regression)

Statistical programming experience in R is an advantage

R Project website also contains extensive documentation.

A basic "getting you started in R" tutorial. Uses the Boston Housing Data (thanks to Giles Hooker).

File translated from T

On 29 Jan 2013, 12:10.