Computational Statistics
Today, nearly every statistical analysis is performed on a computer. Some methods
are particularly dependent on intensive computing or custom software. Biostatistics
faculty are involved with this specialty, known as computational statistics. Some
faculty analyze massive datasets. For example, in functional magnetic resonance imaging
(fMRI) data, a single dataset consists of 100 million elements. Many faculty create
software which is used throughout the world, including tools for the analysis of genetic
data (e.g. for genotype error detection, and for linkage and association analysis
in pedigrees) and brain imaging data (e.g. for nonparametric analysis of PET and fMRI
data). Custom software is necessitated by complex data structures or for graphical
methods for exploring data. Another area of interest to our faculty is permutation
or resampling methods, which allow inferences under weak assumptions, but require
analyzing variations on the data thousands of times over. An essential tool for Bayesian
modeling is Markov Chain Monte Carlo (MCMC). This computationally intensive simulation
procedure is used to characterize complex high-dimensional posterior distributions.
Faculty: V. Baladandayuthapani, P. Boonstra, L. Fritsche, Z. He, N. Henderson, H. Jiang, H.M. Kang, J. Kang, G. Li, Y. Li, J. Morrison, L. Wang, Z. Wu, P. Song, W. Wen, L. Zhao, X. Zhou