My current research interests fall into the following areas of statistics: (i) the development of statistical procedures for analyzing data on workplace exposure to contaminants, (ii) the study of statistical tolerance intervals and regions, (iii) inference in linear mixed and random models, and (iv) applications of higher order asymptotics.

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.