statistical methods for high-dimensional datasets

Dr. Archer’s primary research area has been in the development of statistical methods and computational algorithms for analyzing high-dimensional datasets. Such datasets frequently arise in studies that use high-throughput genomic assays, which yield datasets consisting of a large number of candidate predictors (p, the number of genes or proteins) on a small number of observations (n, the number of samples). Therefore, fitting statistical models for overparameterized problems is an active area of methodological research.