My main research area is inference for regression and stochastic process models. I am particularly interested in flexible semiparametric methods that require much weaker model assumptions than classical methods, which are typically likelihood based. An important focus is on characterizing and constructing estimators that are efficient in the sense of the Hajek and Le Cam theory for locally asymptotically normal families. Getting to the bottom of a problem sometimes leads to a new approach that is surprisingly simple but nonetheless optimal. Several of my recent papers and projects are on efficient estimation in nonparametric and semiparametric missing data models.
