This research program is largely motivated by applications in environmental health. Methods developed in this program exploit the underlying structure offered by the scientific question and the study design. Researchers are often interested in drawing inferences on unknown population parameters (or probability distributions) when the parameters (or probability distributions) are constrained by inequalities. For example, a cancer biologist may be interested in understanding changes in gene expression over "ordered conditions" such as exposure to different doses and/or duration of exposure to a chemical, tumor stages etc. In some instances the inequality constraints may arise naturally on a unit circle instead of the p-dimensional Euclidean space. For instance, cell-cycle experiments are routinely conducted to determine, among other things, the phase angle associated with each cell-cycle gene. Thus in this case the parameter space is described by points on a unit circle. Based on available literature and known biological functions of cell-cycle genes, one may expect an (isotropic) order among the phase angles around the unit circle. In this research program, we are developing methods for analyzing data that exploit such inequalities/order. Nonparametric methods for analyzing ordered multivariate data are also being developed in this research program. The resulting methods are often more powerful and efficient than standard methods. Methods are also being developed in this program for analyzing high dimensional data, such as those arising in genomic studies (e.g. gene expression, CpG methylation), human microbiome and toxicology.
Shyamal Peddada received BSc (Hons) in Mathematics from the University of Delhi, India, MSc in Agricultural Statistics from the Indian Agricultural (Statistics) Research Institute and PhD from University of Pittsburgh in 1983. Before joining the NIEHS in 2000 he as was a tenured full professor of statistics at the University of Virginia, Charlottesville.