Trevor Hefley
Associate Professor
Department of Statistics
Kansas State University
thefley@ksu.edu
Welcome to my research website! Broadly, my research focuses on developing and applying spatio-temporal statistical methods to inform environmental decisions.
Recent News
December 2021: New paper accepted in Transboundary and Emerging disease assesses the Diagnostic sensitivity of biological samples for detecting African swine fever virus infection.
July 2021: New paper accepted in Ecology that demonstrates how to embed machine learning algorithms into Bayesian occupancy models that account for spatial autocorrelation.
Mohankumar, N.M., and T.J. Hefley. (in press) Using machine learning to identify nontraditional spatial dependence in occupancy data. Ecology.
July 2021: New paper accepted in Crop Science that documents the impact of tillers on yield in corn.
Veenstra, R.L., C. Messina, D. Berning, L. Haag, P. Carter, T. Hefley, V. Prasad I. Ciampitti. (in press) Effect of tillers on corn yield: Exploring trait plasticity potential in unpredictable environments. Crop Science [pdf]
June 2021: New paper accepted in Plant Methods that reviews, explains and demonstrates different parameter estimation techniques for the light extinction coefficient.
Lacasa, J., T.J. Hefley, F. Curin, M.E Otegui, I.A. Ciampitti. (in press) A practical guide to estimating the light extinction coefficient with nonlinear models – an example in maize. Plant Methods [pdf]
May 2021: New paper accepted in Spatial Statistics that shows how to recover individual-level inference from aggregated binary data.
Walker, N.B., T.J. Hefley, A.E. Ballmann, R.E. Russell, D.P. Walsh. (in press) Recovering individual-level spatial inference from aggregated binary data. Spatial Statistics [pdf]
May 2021: New paper accepted in Agricultural Systems Journal that reviews and compares regression approaches to evaluate predictions from crop models.
Correndo, A.A., T.J. Hefley, D. Holzworth, D., I.A. Ciampitti. (in press) Revisiting linear regression to test agreement in continuous predicted-observed datasets. Agricultural Systems Journal [pdf]