Ephraim M. Hanks
Hivatkozott rá
Hivatkozott rá
Spatial autoregressive models for statistical inference from ecological data
JM Ver Hoef, EE Peterson, MB Hooten, EM Hanks, MJ Fortin
Ecological Monographs 88 (1), 36-59, 2018
Restricted spatial regression in practice: geostatistical models, confounding, and robustness under model misspecification
EM Hanks, EM Schliep, MB Hooten, JA Hoeting
Environmetrics 26 (4), 243-254, 2015
Continuous-time discrete-space models for animal movement
EM Hanks, MB Hooten, MW Alldredge
Agent-based inference for animal movement and selection
MB Hooten, DS Johnson, EM Hanks, JH Lowry
Journal of Agricultural, Biological and Environmental Statistics 15, 523-538, 2010
Circuit Theory and Model-Based Inference for Landscape Connectivity
EM Hanks, MB Hooten
Journal of the American Statistical Association 108 (501), 22-33, 2013
On the relationship between conditional (CAR) and simultaneous (SAR) autoregressive models
JM Ver Hoef, EM Hanks, MB Hooten
Spatial statistics 25, 68-85, 2018
Velocity-based movement modeling for individual and population level inference
EM Hanks, MB Hooten, DS Johnson, JT Sterling
PLoS One 6 (8), e22795, 2011
Hierarchical animal movement models for population‐level inference
MB Hooten, FE Buderman, BM Brost, EM Hanks, JS Ivan
Environmetrics 27 (6), 322-333, 2016
Reconciling resource utilization and resource selection functions
MB Hooten, EM Hanks, DS Johnson, MW Alldredge
Journal of Animal Ecology 82 (6), 1146-1154, 2013
Social, spatial and temporal organization in a complex insect society
LE Quevillon, EM Hanks, S Bansal, DP Hughes
Scientific reports 5 (1), 13393, 2015
Animal movement constraints improve resource selection inference in the presence of telemetry error
BM Brost, MB Hooten, EM Hanks, RJ Small
Ecology 96 (10), 2590-2597, 2015
Effects of two centuries of global environmental variation on phenology and physiology of Arabidopsis thaliana
VL DeLeo, DNL Menge, EM Hanks, TE Juenger, JR Lasky
Global Change Biology 26 (2), 523-538, 2020
Temporal variation and scale in movement-based resource selection functions
MB Hooten, EM Hanks, DS Johnson, MW Alldredge
Statistical Methodology 17, 82-98, 2014
The Bayesian group lasso for confounded spatial data
TJ Hefley, MB Hooten, EM Hanks, RE Russell, DP Walsh
Journal of Agricultural, Biological and Environmental Statistics 22, 42-59, 2017
Reconciling multiple data sources to improve accuracy of large‐scale prediction of forest disease incidence
EM Hanks, MB Hooten, FA Baker
Ecological applications 21 (4), 1173-1188, 2011
Machine learning for modeling animal movement
DA Wijeyakulasuriya, EW Eisenhauer, BA Shaby, EM Hanks
Plos one 15 (7), e0235750, 2020
Dynamic spatio-temporal models for spatial data
TJ Hefley, MB Hooten, EM Hanks, RE Russell, DP Walsh
Spatial statistics 20, 206-220, 2017
Confronting models with data: The challenges of estimating disease spillover
PC Cross, DJ Prosser, AM Ramey, EM Hanks, KM Pepin
Philosophical Transactions of the Royal Society B 374 (1782), 20180435, 2019
Dynamic models of animal movement with spatial point process interactions
JC Russell, EM Hanks, M Haran
Journal of Agricultural, Biological, and Environmental Statistics 21, 22-40, 2016
A spatially varying stochastic differential equation model for animal movement
JC Russell, EM Hanks, M Haran, D Hughes
A rendszer jelenleg nem tudja elvégezni a műveletet. Próbálkozzon újra később.
Cikkek 1–20