Trevor J. Hefley

Assistant professor

Department of Statistics 

 Kansas State University

thefley@ksu.edu

 





Welcome to my research website!  Broadly, my research focuses on developing and applying statistical methods to inform wildlife conservation and management decisions. I am also interested in applied statistics, specifically, how other disciplines such as engineering, biology, ecology and computer science utilize the large amounts of data that are currently available for decision making.


Recent News: 

  • April 5, 2017: New paper accepted in Methods in Ecology and Evolution that corrects for bias caused by location error in species distribution models.
    • Hefley, T.J., B.M. Brost, M.B. Hooten (In press) Bias correction of bounded location errors in presence­-only data. Methods in Ecology and Evolution [pdf] [R code S1 & S3]
    • March 31, 2017: New paper published in Ecology Letters that uses partial differential equations to make ecological forecasts.
      • Hefley, T.J., M.B. Hooten, R.E. Russell, D.P. Walsh, J.A., Powell (In press). When mechanism matters: Bayesian forecasting using models of ecological diffusion. Ecology Letters [pdf] [R code S1 & S2]
      • March 11, 2017: New paper published in Ecology.
        • Hefley, T.J., K.M. Broms, B.M. Brost, F.E. Buderman, S. Kay, H.R. Scharf, J.R. Tipton, P.J., Williams, M.B. Hooten (2017) The basis function approach for modeling autocorrelation in ecological data. Ecology 98:632-646. [pdf] [R code S3, S4, & S5]
      • March 11, 2017: New paper published in Ecology.
        • Williams, P.J., M.B. Hooten, J.N Womble, G.G Esslinger, M.R. Bower, T.J. Hefley. (2017) An integrated data model to estimate spatio-temporal occupancy, abundance, and colonization dynamics. Ecology 98:328-336. [pdf]
      • March 1, 2017: New paper published in the Journal of Agricultural, Biological, and Environmental Statistics that uses a group lasso penalty to regularize spatial generalized linear mixed models in the presence of confounding.
        • Hefley, T.J., M.B. Hooten, E.M. Hanks, R.E. Russell, D.P. Walsh (In press) The Bayesian group lasso for confounded spatial data. Journal of Agricultural, Biological, and Environmental Statistics 22:42-59. [pdf] [R code]
      • February 16, 2017: New paper accepted in Spatial Statistics that develops a framework for using dynamic spatio-temporal models when modeling spatial autocorrelation.
        • Hefley, T.J., M.B. Hooten, E.M. Hanks, R.E. Russell, D.P. Walsh (In press) Dynamic spatio-temporal models for spatial data. Spatial Statistics [pdf] [R code]