Casey Jelsema, Ph.D.
Biostatistics & Computational Biology Branch
Casey M. Jelsema, Ph.D.
Fellow – Research
Casey Jelsema earned a doctorate in Statistics from Western Michigan University in 2013. That same year he joined NIEHS as a postdoctoral research fellow under the supervision of Shyamal Peddada, where he collaborates on statistical methodological research and NIEHS’ Gulf Long-term Followup Study (GuLF STUDY).
Casey’s methodological research focuses on reduced rank geostatistical modeling for very large data sets as well as constrained inference for linear models. A common motivation in much of this work has been to address situations when the data deviate from the Normal distribution. Casey is primarily interested in exploring environmental and ecological applications, but his research is broadly applicable. Other interests include Bayesian hierarchical modeling and computational statistics.
- 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.