Stat 314: Empirical Likelihood

Overview

Empirical likelihood (EL) allows likelihood based inferences without assuming any parametric form for the likelihood. It is based instead on reweighting the sample values. It provides data driven shapes for confidence regions and confidence bands. EL tests have competitive power. This course covers: nonparametric maximum likelihood and likelihood ratios, censoring and truncation, biased sampling, estimating equations, GMM, Bayesian bootstrap, Euclidean and Kullback-Leibler log likelihoods and recent research directions.

Instructor

Art Owen

Sequoia Hall 130

My userid is owenpelican on stanfordpelican.edu (remember to remove the waterfowl)

Office hour: Weds 11am

TA

Jingshu Wang

Sequoia Hall TBD

jingshuwpenguin@stanfordpenguin.edu (remember to delete Antarctic birds)

Office hour: Friday 9:30 - 11:30

Readings

We will use the book ``Empirical Likelihood'' and some articles posted online.

Recent work on log concave MLEs

• Gunther Walther's survey
• Cule and Samworth's multidimensional theory
• logconcdead software paper

Lecture notes

• lecture 3
• lecture 4
• lecture 5
• lecture 6
• lecture 7
• lecture 8

Problem sets

• Problem set 1 Duck data| documentation
  Worm data
  Mildly paranoid empirical likelihood code:
  emplik.R | mllog.R | svdlm.R
  based on this article
• Problem set 2

Evaluation

Evaluation is based on problem sets and a project.