- New paper on how to infer sources in mechanistic spatio-temporal models:
Okasaki, C., M.B. Hooten, and A.M. Berdahl. (In Press). Source reconstruction for spatio-temporal physical statistical models. Spatial Statistics.
- New paper accepted on mechanistic spatio-temporal models for spatial data:
Wright, W.J., P.N. Neitlich, A.E. Shiel, and M.B. Hooten. (In Press). Mechanistic spatial models for heavy metal pollution. Environmetrics.
- New paper with insights on statistical models for binary data:
Scharf, H.R., X. Lu, P.J. Williams, and M.B. Hooten. (2022). Constructing flexible, identifiable, and interpretable statistical models for binary data. International Statistical Review, 90: 328-345.
- New paper on community confouding:
Van Ee, J.J., J.S. Ivan, and M.B. Hooten. (2022). Community confounding in joint species distribution models. Scientific Reports, 12: 12235.
- New paper on inverse reinforcement learning for animal movement:
Schafer, T.L.J., C.K. Wikle, and M.B. Hooten. (2022). Bayesian inverse reinforcement learning for collective animal movement. Annals of Applied Statistics, 16: 999-1013.
- New paper on fast multi-stage computing:
Johnson, D.S., B.M. Brost, and M.B. Hooten. (2022). Greater than the sum of its parts: Computationally flexible Bayesian hierarchical modeling. Journal of Agricultural, Biological, and Environmental Statistics, 27: 382-400.
- New paper on landscape genetics:
Zimmerman, S., C. Aldridge, S. Oyler-McCance, and M.B. Hooten. (2022). Scale-dependent influence of the sagebrush community on genetic connectivity of the sagebrush obligate Gunnison sage-grouse. Molecular Ecology, 31: 3267-3285.