In addition, I became involved in several projects at NISS which are ongoing areas of research. To support my work in computational statistics, I have been actively involved with my colleague Dongchu Sun in building and maintaining the departmental computing network of Sun workstations. Our expanding network has been vital in ongoing research in the department in nonparametric regression, Bayesian statistics, wavelets, isotonic inference, time series and reliability. In an article to appear in JASA, Peter Hall, Catherine Huber and I derived a new method for assessing differences in treatment groups in the presence of a single covariate when the usual hypotheses are suspect. Bootstrapping contributes to the small sample performance, and simulation shows that the method approaches its asymptotic superiority to the classic test statistic for moderate sample sizes.
Current work focuses on extending to multiple covariates and more complicated models.
Additive models in Longitudinal Data Analysis Motivated by an experiment monitoring milk production of cows over three summer months, doctoral student Sungwook Lee and I are studying studying additive models for longitudinal data. Moyeed and Diggle extended my 1988 JRSS B approach to estimation in partially linear models to the case of correlated data. We are currently studying other approaches to the problem and analyzing situations with more than one semiparametric term. For example, a probabilistic model might be constructed to predict the type and location of activities in a day for a person in with specific demographic characteristics living in one part of a city . Working with Eric Pas in the Engineering School at Duke University, Kenneth Vaughn in the Department of Transportation, and Dongchu Sun at MU, I have developed a new model for activity location choice.