In the context of analyzing occupational exposure data, my work is on the development of statistical methodologies that are better suited and more accurate for exposure monitoring in a wide variety of workplace environments. This is critical for setting exposure limits and for assessing occupational risk. The relevant data are usually lognormally distributed, and mixed and random effects models are very often appropriate. The problems of interest here deal with the development of tests and confidence regions concerning one or more lognormal means, the computation of tolerance intervals, and the development of techniques to deal with data below the detection limits. This work has been funded through an NIH grant from National Institutes of Occupational Safety and Health.

I am interested in all aspects of statistical inference concerning linear mixed and random effects models. My research interests in this area include the development of tests and confidence intervals concerning various parametric functions involving fixed effects and variance components. My ongoing work on the development of tolerance regions, univariate as well as multivariate, extend to mixed and random effects models as well. I am also interested in the development of tolerance intervals under discrete models.

I have also been investigating the application of higher order asymptotic theory for a variety of inference problems, including the computation of tolerance intervals in mixed and random effects models. Higher order asymptotics appears to be a satisfactory option for computing tolerance factors in very general mixed and random effects models, even if the data are unbalanced.