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 regions and calibration procedures, the development of techniques to deal with data below the detection limits, and the study of biomarkers and biological monitoring. This work is funded through a grant from the National Institutes of Health.
I am interested in all aspects of statistical inference concerning linear mixed and random effects models. At present, my research interest in this area is on the development of tests and confidence intervals concerning `non-standard' parametric functions involving fixed effects and variance components. The problems came up in the analysis of Army test data at the Army Research Laboratory, Aberdeen Proving Ground, Maryland. My work in this area was funded by the U. S. Army Research Office. My ongoing work on the theoretical development and computation of tolerance regions, univariate as well as multivariate, extend to mixed and random effects models as well. Jointly with a collaborator, I am currently in the process of writing a book on tolerance regions.
In the area of bioequivalence, my interests are on the development of efficient and
easy to use tests concerning all aspects of bioequivalence, starting with average
bioequivalence.
Of particular interest is the development of test procedures and sample size determination,
when a
pilot bioequivalence trial is followed by a pivotal trial. I am also actively involved in the
investigation of multivariate bioequivalence--a topic on which very little has been done.