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.
- Art Owen
- Sequoia Hall 130
- My userid is owenpelican on stanfordpelican.edu (remember to remove the waterfowl)
- Office hour: Weds 11am
- Jingshu Wang
- Sequoia Hall TBD
- jingshuwpenguin@stanfordpenguin.edu (remember to delete Antarctic birds)
- Office hour: Friday 9:30 - 11:30
We will use the book ``Empirical Likelihood'' and some articles posted online.
Evaluation is based on problem sets and a project.