STEVEN N. MACEACHERN

My current research focuses on several areas. I am in the process of developing and applying nonparametric Bayesian models that incorporate covariate information in a natural sense. These probability models for a random distribution create a framework whereby a collection of distributions is allowed to evolve in a continuous fashion as a covariate changes. The models, used as prior distributions in a Bayesian context, are fit with Markov chain Monte Carlo techniques. This leads to a second area of research, namely how to most effectively use the output of a simulation. Iyue Sung (Ph.D. 2001) has looked at how to more effectively estimate densities when importance sampling weights are present while Subha Guha (current Ph.D. student) has examined how to improve estimators based on a subsample of Markov chain output. I am also involved in the active group at Ohio State working on ranked set sampling, with an eye toward creating more robust inferential methods and eventually connecting ranked set sampling to Bayesian methods.