Casey Jelsema, Ph.D.
Biostatistics & Computational Biology Branch
Casey M. Jelsema, Ph.D.
Fellow – Research
Casey Jelsema, Ph.D., earned a doctorate in statistics from Western Michigan University in 2013, and subsequently joined NIEHS as a postdoctoral research fellow under the supervision of Shyamal Peddada, Ph.D. He conducts original research on statistical methodology as well as the NIEHS Gulf Long-term Followup Study (GuLF STUDY).
In his dissertation, Jelsema's research focused on reduced rank geostatistical modeling for very large data sets. In the course of this work he used both Empirical Bayesian and fully Bayesian methods. Since his arrival at NIEHS, Jelsema has continued to work on reduced rank spatial models, and has also conducted research in the areas of constrained inference for linear models and bootstrap methodology.
A common motivation in much of Jelsema's work has been to address situations when the data deviate from the Normal distribution. For the future, he is most interested in developing methods with applications relating to ecology, or the environment and conservation thereof; however, his research is broadly applicable to many areas. Other interests of his include Statistical computing and Bayesian hierarchical modeling.
- Paul R, Jelsema CM, and Lau KW (2014). A flexible class of reduced rank spatial models for large non-Gaussian datasets. Accepted, peer reviewed Book Chapter in “Current Trends in Bayesian Methodology with Applications” edited jointly by Dipak K. Dey, Umesh Singh and A. Loganathan. To be published by Chapman & Hall/CRC Press.
- Jelsema CM and Paul R (2013). Spatial Mixed Effects Models for Compositional Data with Applications to Coal Geology. International Journal of Coal Geology, 114, 33-43. DOI: 10.1016/j.coal.2013.04.004.