ABSTRACT

This chapter describes how to approach the optimization problems posed by empirical likelihood. The emphasis is on computing the empirical likelihood, for statistics defined through estimating equations, with nuisance parameters and side constraints. Much of the discussion carries over to other nonparametric likelihoods.