Computational Statistics, Nonparametric Function Estimation, Spatial Statistics, Time Series Analysis, Data Mining, Computational Statistics, Monte Carlo Methods, Environmetrics, Bayesian Methods, Applied Statistics, Theoretical Statistics and Probability Theory
Professor Cox's primary research interests are nonparametric function estimation, stochastic processes, statistical computing, and the application of statistical methods to complex scientific and technological problems, such as testing theories against experimental data when computational complexity limits the number of theoretical predictions that can be obtained.
Many such problems involve estimation of unknown constants or even unknown functions. Professor Cox has done fundamental research in the use of spline functions for such applications and extensive work in developing methodologies and practical computational approaches. He has collaborated with investigators from such disciplines as electrical engineering, neurophysiology, oncology, and economics but currently is most active in applications in nuclear fusion research. He expects that there will be applications for such methodologies in materials science and atmospheric science as well. Professor Cox has also done theoretical work in nonparametric function estimation, statistical approximation theory, and Bayesian methods.