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Math and Stat Profile

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Brook Russell

Mathematical and Statistical Sciences

Associate Professor

Martin Hall O220 [Office]


Educational Background

PhD, Statistics, Colorado State University
MS, Statistics, Colorado State University
MA, Mathematics, University of Montana
BA, Mathematics, William Jewell College

Research Interests

Developing novel statistical methods to leverage environmental data in order to inform our understanding of the Earth's climate system. Special focus is given to modeling events in the far upper tails of response distributions, with a particular interest in innovative univariate and multivariate extreme value methodology.

Selected Publications

Russell, B.T., Cressman, K.A., Schmit, J.P., Shull, S., Rybczyk, J.M., Frost, D.L. (2021) How should surface elevation table data be analyzed? A comparison of several commonly used analysis methods and one newly proposed approach. Accepted, Environmental and Ecological Statistics.

Brown-Steiner, B., Zhou, X., Alvarado, M.J., Russell, B.T. (2021). Prediction of High-ozone Events Using GAM, SMOTE, and Tail Dependence Approaches in Texas (2005-2019). Aerosol Air Qual. Res.

Russell, BT, Porter WC (2021). Using spatial smoothing to model a functional regression estimator to points on a lattice with application to surface-level ozone in the Eastern United States. Environmental and Ecological Statistics

Self, SW, McMahan, CS, Russell, BT. Identifying meteorological drivers of PM2.5 levels via a Bayesian spatial quantile regression. Environmetrics. 2021; 1– 20.

Russell, BT, Huang, WK. Modeling short-ranged dependence in block extrema with application to polar temperature data. Environmetrics. 2020;e2661.

Russell, BT, Risser, MD, Smith, RL, Kunkel, KE (2019). Investigating the association between late spring Gulf of Mexico sea surface temperatures and U.S. Gulf Coast precipitation extremes with focus on Hurricane Harvey. Environmetrics. 2019;e2595.

Russell, B., Hogan, P. (2018). Analyzing dependence matrices to investigate relationships between national football league combine event performances. Journal of Quantitative Analysis in Sports, doi:10.1515/jqas-2017-0086

Russell, B.T. (2018). Investigating Precipitation Extremes in South Carolina with focus on the State's October 2015 Precipitation Event. Journal of Applied Statistics, DOI: 10.1080/02664763.2018.1477926.

Fix, M.J., Cooley, D., Hodzic, A., Gilleland, E., Russell, B.T., Porter, W.C., and Pfister, G.G. (2018). Observed and predicted sensitivities of extreme surface ozone to meteorological drivers in three US cities. Atmospheric Environment, Volume 176, March 2018, Pages 292-300, ISSN 1352-2310,

Russell, B.T., Wang D., and McMahan, C.S. (2017). Spatially Modeling the Effects of Meteorological Drivers of PM 2.5 in the Eastern United States via a Local Linear Penalized Quantile Regression Estimator. Environmetrics, 28:e2448, doi:10.1002/env.2448.

Russell, B.T., and Dyer, J.L. (2017). Investigating the link between PM2.5 and atmospheric profile variables via penalized functional quantile regression. Environmental and Ecological Statistics, 24:363--384, doi:10.1007/s10651-017-0374-2.

Russell, B.T., Cooley, D.S, Porter, W.C., Reich, B.J., and Heald, C.L. (2016). Data mining to investigate the meteorological drivers for extreme ground level ozone events. Annals of Applied Statistics, 10(3), 1673–1698, doi:10.1214/16-AOAS954.

Russell, B.T., Cooley, D.S., Porter, W.C., and Heald, C.L. (2016) Modeling the spatial behavior of the meteorological drivers’ effects on extreme ozone. Environmetrics, 27(6): 334-344, doi:10.1002/env.2406.

Porter, W.C., Heald C.L., Cooley D., and Russell, B., (2015). Investigating the observed sensitivities of air quality extremes to meteorological drivers via quantile regression. Atmospheric Chemistry and Physics Discussions, 15(10), 14075-14109, doi:10.5194/acp-15-10349-2015.


American Statistical Association


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