Dr. Robert Erhardt
Assistant Professor of Statistics
Wake Forest University
Office: 342 Manchester
E-mail: erhardrj “at” wfu.edu
About: My research interests include environmental and climate statistics, computational statistics, extremes, and actuarial science.
I'm particularly interested in measuring and quantifying environmental risks, studying impacts of climate change on these risks, and exploring possible insurance solutions.
My research is funded by the The Research Grants Task Force of the Casualty Actuarial Society, and from the Society of Actuaries I am funded under the programs of the Committee on Knowledge Extension Research, the Climate and Environmental Research Sustainability Committee, and the Research Expanding Boundaries (REX) Pool.
I completed my doctorate in Statistics and Operations Research from the University of North Carolina at Chapel Hill. I also completed degrees from the State University of New York College at Geneseo and the University of Wisconsin-Madison. In 2010 I became an Associate of the Casualty Actuarial Society.
I'm originally from a small town in the Hudson river valley of New York State, and love all things "upstate." In my spare time I enjoy backpacking and woodworking.
A recent CV can be found here: ErhardtCV.
Working Manuscripts Available:
1. Jin, Z. and Erhardt, R. Incorporating Climate Change Projections into Risk Measures of Index-Based Insurance.
Published and Accepted Papers:
17. Erhardt, R., Engler, D. An Extension of Spatial Dependence Models for Estimating Short-Term Temperature Portfolio Risk. North American Actuarial Journal (accepted).
16. Erhardt, R. (2017). Climate, Weather, and Environmental Sources for Actuaries. Society of Actuaries. A 78 page white paper on sources for actuarial environmental risk measurement, written for the Society of Actuaries.
15. Anderson, T.M., White, S., Davis**, B., Erhardt, R., Palmer, M., Swanson, A., Kosmala, M., and Packer, C. (2016). The spatial distribution of African savannah herbivores: species associations and habitat occupancy in a landscape context. Philosophical Transactions of the Royal Society B, 371:1702
14. Johnson*, D. and Erhardt, R. (2016) Projected Impacts of Climate Change on Wind Energy Density in the United States. Renewable Energy 85, 66-73.
13. Erhardt, R. (2015). Incorporating Spatial Dependence and Climate Change Trends for Measuring Long-Term Temperature Derivative Risk. Variance 9:2, 213-226
12. Erhardt, R., Shuman, M. (2015) Assistive Technologies for Second-Year Statistics Students who are Blind. Journal of Statistics Education 23:2, 1-28.
11. Steel*, A., Erhardt, R., Phelps, R, Upham, P. (2015). Estimates of Enhanced Outcomes in Employment, Income, Health and Volunteerism for The Association of Boarding Schools Member School Graduates. Journal of Advanced Academics. 26:3, 227-245
10. Erhardt, R., Smith, R., Lopes, B., Band, L. (2015). Statistical downscaling of precipitation on a spatially dependent network using regional climate models, Stochastic Environmental Research and Risk Assessment 29:7, 1835-1849. DOI 10.1007/s00477-014-0988-y
9. Erhardt, R. (2015) Mid-twenty-first-century projected trends in North American heating and cooling degree days, Environmetrics 26(2):133-144, doi: 10.1002/env.2318.
8. Erhardt, R., Smith, R. (2014). Weather derivative risk measures for extreme events, North American Actuarial Journal, Vol 18:3, 379-393.
7. Godfrey, A.J.R., Erhardt, R. (2013). Addendum to Statistical Software from a Blind Person's Perspective. The R Journal, 5(1):73-80, 2013. URL http://journal.r-project.org/archive/2013-1/godfrey.pdf. [p]
6. Cooley, D., Cisewski, J., Erhardt, R., Jeon, S., Mannshardt, E., Omolo, B., Ying, S. (2012). A survey of spatial extremes: measuring spatial dependence and modeling spatial effects. REVSTAT Vol 10:1, 135-165.
5. Erhardt, R., Smith, R. (2012). Approximate Bayesian computing for spatial extremes. Computational Statistics and Data Analysis Vol. 56:6, 1468-1481.
4. Stupar, R., Bhaskar, P., Yandell, B., Rensink, W., Hart, A., Ouyang, S., Veilleux, R., Busse, J., Erhardt, R., Buell, C., Jiang, J. (2007). Phenotypic and transcriptomic changes associated with potato autopolyploidization, Genetics Vol. 176, 2055-2067.
3. De Stasio, G., Rajesh, D., Ford, J., Daniels, M., Erhardt, R., Frazer, B., Tyliszczak, T., Gilles, M., Conhaim, R., Howard, S., Fowler, J., Esteve, F., Mehta, M. (2006). Motexafin-gadolinium taken up in vitro by at least 90\% of glioblastoma cell nuclei, Clinical Cancer Research 12; 206.
2. De Stasio, G., Rajesh, D., Casalbore, P., Daniels, M., Erhardt, R., Frazer, B., Wiese, L., Richter, K., Sonderegger, B., Gilbert, B., Schaub, S., Cannara, R., Crawford, J., Gilles, M., Tyliszczak, T., Fowler, J., Larocca, L., Howard, S., Mercanti, D., Mehta, M., Pallini, R. (2005). Are Gadolinium contrast agents suitable for gadolinium neutron capture therapy? Neurological Research, Vol. 27 No. 4 pp. 387-398
1. Freeman, C., Burke, D., Erhardt, R., DeCiantis, J., Padalino, S., Knauer, J. (2003). Thin foil calorimeter calibration using a 2 MV Van de Graaff accelerator, Rev. Sci. Instrum. 74, 1921.
* Wake Forest University undergraduate student
** Wake Forest University graduate student
1. Erhardt, R., Sisson, S. Modelling extremes using approximate Bayesian computation, In Extreme Value Modelling and Risk Analysis: Methods and Applications. Eds. D. Dey and J. Yan. Chapman & Hall/CRC Press
Teaching at Wake Forest:
MST 767 Generalized Linear Models (Fa17)
MST 369/669 Time Series and Forecasting (Fa16)
MST 367/667 Linear Models (Sp15, Sp17, Fa17)
MST 362/662 Multivariate Statistics (Sp16, Fa16)
MST 358/658 Mathematical Statistics (Sp13, Sp14, Sp17)
MST 353/653 Probability Models (Sp13, Sp16)
MST 256/656 Statistical Models (Fa13, Sp14, Sp15, Fa15)
MST 109 Elementary Probability and Statistics (Fa12, Fa15)
SUS 602 Scientific Literacy (co-taught Ja15, Ja16, Su16, Su17)
3. Climate Change Risks and Opportunities for Actuaries (CAS Spring Meeting 2017 Toronto, ON)
2. Assistive Technologies for Second Year Statistics Students who are Blind (video webinar)
1. Intro Climate Talk
5. R scripts and data for short-term Temperature Portfolio Risk: Scripts and Data
4. Code for Modelling extremes using approximate Bayesian computation in Exterme Value Modeling and Risk Analysis --- Methods and Applications
3. R package ABCExtremes (this facilitates ABC fitting of isotropic, stationary max-stable processes following the Erhardt, Smith 2012 CSDA paper) ABCExtremes.tar.gz
2. R scripts and data for K-nn downscaling hydrology application: Scripts and Data for Downscaling Paper
1. Data and scripts for North American Cooling Degree Day and Heating Degree Day Trends: Data and Files for North American CDD and HDD Paper
Actuarial Science Advice at Wake:
1. Actuarial Science Advice at Wake Forest
Public and Media:
1. Climate Central article
3. I briefly appear on screen in the 2009 film Public Enemies; this gives me a Bacon number of 4. As a statistician I have an Erdös number of 3. Combined, this gives me an Erdös-Bacon number of 7.
2. My house appears in the 2014 film Goodbye to All That.
1. I am red green colorblind.