- PhD, Western Michigan University, Kalamazoo, MI, 2013
- MS, Western Michigan University, Kalamazoo, MI, 2011
- BS, Grand Valley State University, Allendale, MI, 2008
Hessl, A.E., Anchukaitis, K.J., Jelsema, C., Cook, B., Byambasuren, O., Leland, C., Nachin, B., Pederson, N., Tian, H., Andreu-Hayles, L. (2018). Past and future drought in Mongolia. Science Advances, 4(3) e1701832, DOI: 10.1126/sciadv.1701832
Davidov, O., Jelsema, C., and Peddada, S. (2018). Testing for inequality constraints in singular models by trimming or winsorizing the variance matrix. Journal of the American Statistical Association, 113:522, 906-918, DOI: 10.1080/01621459.2017.1301258
Law, E., Morris, K., and Jelsema, C. (2017). Determining the Number of Test Fires Needed to Represent the Variability Present within 9 mm Luger Firearms. Forensic Science International, 276, 126-133, DOI: doi.org/10.1016/j.forsciint.2017.04.019
Jelsema, C. and Peddada, S. (2016). CLME: An R Package for Linear Mixed Effects Models Under Inequality Constraints. Journal of Statistical Software, 75(1), 1-32, DOI: 10.18637/jss.v075.i01
van't Erve, T.J., Lih F., Jelsema, C., Deterding, L., Eling, T., Mason, R., Kadiiskaa, M. (2016). Reinterpreting the best biomarker of oxidative stress: The 8-iso-prostaglandin F2α/prostaglandin F2α ratio shows complex origins of lipid peroxidation biomarkers in animal models. Free Radical Biology and Medicine. Vol 95. 65-73, DOI: doi:10.1016/j.freeradbiomed.2016.03.001
Paul, R., Jelsema, C., and Liu, R. (2015). A flexible class of reduced rank spatial models for large non-Gaussian datasets. In: Current Trends in Bayesian Methodology with Applications. Edited by Satyanshu K. Upadhyay, Umesh Singh, Dipak K. Dey, and Appaia Loganathan. Chapman and Hall/CRC 2015. Pages 477-502. DOI: 10.1201/b18502-24
Jelsema, C. and Paul, R. (2013). Spatial mixed effects model for compositional data with applications to coal geology. International Journal of Coal Geology. Vol 114. 33-43. DOI: https://doi.org/10.1016/j.coal.2013.04.004
About Casey Jelsema
I received my PhD in Statistics from Western Michigan University in 2013, where my dissertation investigated several approaches to dealing with contaminated or skewed data in large geostatistical datasets. Geostatistics is a subset of spatial statistics, the branch of statistics in which the nearness of data in location imparts a correlation or dependence.
I then moved to the National Institute of Environmental Health Sciences (NIEHS) as a postdoctoral fellow. There, I was introduced to the area of constrained inference and wrote an R package (CLME) implementing this for linear fixed or mixed effects models.
I joined the faculty of WVU in 2015 as an Assistant Professor. Since then I have continued to research in the areas of both spatial statistics and constrained inference. I have also worked on collaborative and interdisciplinary projects including with Geography and Forensic Science.
Since joining the Biostatistics department in the School of Public Health, I have also begun collaborations with Epidemiologists, physicians in the Department of Anesthesiology, in the School of Medicine, and more.
An R package for linear mixed effects models where (some of) the fixed-effects coefficients are subject to constraints. Inference is performed using residual bootstrap methodology.
The official package is available on CRAN (https://cran.r-project.org/web/packages/CLME/index.html). See the Github page (https://github.com/jelsema/CLME) for developmental version and bug reports.
Other software projects may be available the my Github page (https://github.com/jelsema).