Dr. Megan Heyman is a statistician with expertise in nonparametric statistical methods and time-series data analysis. Her research primarily focuses on developing statistical models to describe climate change and environmental phenomena. She has collaborated in research with NASA’s Jet Propulsion Laboratory and, outside of teaching, enjoys statistical consulting. She is a Rose-Hulman alumna and has served as a co-director for the Independent Project/ Research Opportunity Program.
Academic Degrees
- PhD, University of Minnesota, Statistics, 2016
- MS, University of Minnesota, Statistics, 2014
- BS, Rose-Hulman Institute of Technology, 2008
Awards & Honors
Research Interests
- Bootstrapping methods for spatio-temporal data
- Modeling environmental data with nonparametric statistics
Select Publications & Presentations
Heyman, M., St. George, S., and Chatterjee, S. “Quantifying Spatial and Temporal Relationships Among Tree-Ring Records.” Statistics and Applications. 2020. Vol. 18(2): 163-185
Heyman, M. “Exploring statistical inference for population means through rubber chickens.” Teaching Statistics. 2019. Vol. 41(3): 110-114.
https://doi.org/10.1111/test.12202
Heyman, M. “lmboot: Bootstrap in Linear Models.” R package version 0.0.1, 2019. https://CRAN.R-project.org/package=lmboot
Heyman, M. and Chatterjee, S., WiSEBoot: Wild Scale-Enhanced Bootstrap, R Package Version 1.4.0, 2016
Braverman, A., Chatterjee, S., Heyman, M., and Cressie, N., Probabilistic Evaluation of Competing Climate Models, Being Published in 2016
Heyman, M. and Chatterjee, S., “Predicting Crop Yield via Partial Linear Model with Bootstrap,” Machine Learning and Data Mining approaches to Climate Science (Proceedings of the Fourth International Workshop on Climate Informatics), 2015
Teaching Interests
- Statistics
- Probability