引用次数
引用: 1,931
H-指数: 24
I10-指数 : 40
出版物: 76
On the Relationship between Conditional (CAR) and Simultaneous (SAR) Autoregressive Models
Ephraim M. Hanks , Mevin B. Hooten , Mevin B. Hooten , Jay M. Ver Hoef
arXiv: Statistics Theory
68
2017
Improving Accuracy of Large-Scale Prediction of Forest Disease Incidence Through Bayesian Data Reconciliation
Ephraim M. Hanks
2010
Continuous-time discrete-space models for animal movement
Ephraim M. Hanks , Mevin B. Hooten , Mat W. Alldredge
The Annals of Applied Statistics 9 ( 1) 145 -165
60
2015
A Sample Covariance-Based Approach For Spatial Binary Data
Ephraim M. Hanks , Lin Lin , Sahar Zarmehri
Journal of Agricultural Biological and Environmental Statistics 26 ( 2) 1 -30
2021
Machine learning for modeling animal movement.
Dhanushi A. Wijeyakulasuriya , Elizabeth W. Eisenhauer , Benjamin A. Shaby , Ephraim M. Hanks
PLOS ONE 15 ( 7)
1
2020
Circuit Theory and Model-Based Inference for Landscape Connectivity
Ephraim M. Hanks , Mevin B. Hooten
Journal of the American Statistical Association 108 ( 501) 22 -33
40
2013
Modeling Spatial Covariance Using the Limiting Distribution of Spatio-Temporal Random Walks
Ephraim M. Hanks
Journal of the American Statistical Association 112 ( 518) 497 -507
14
2017
Latent spatial models and sampling design for landscape genetics
Ephraim M. Hanks , Mevin B. Hooten , Steven T. Knick , Sara J. Oyler-McCance
The Annals of Applied Statistics 10 ( 2) 1041 -1062
7
2016
Effects of two centuries of global environmental variation on phenology and physiology of Arabidopsis thaliana
Victoria L. DeLeo , Duncan N. L. Menge , Ephraim M. Hanks , Thomas E. Juenger
bioRxiv 424242
41
2018
Identifying and characterizing extrapolation in multivariate response data.
Meridith L. Bartley , Ephraim M. Hanks , Erin M. Schliep , Patricia A. Soranno
PLOS ONE 14 ( 12)
1
2019
Reconciling resource utilization and resource selection functions.
Mevin B. Hooten , Ephraim M. Hanks , Devin S. Johnson , Mat W. Alldredge
Journal of Animal Ecology 82 ( 6) 1146 -1154
44
2013
A novel quantitative framework for riverscape genetics.
Shannon L. White , Ephraim M. Hanks , Tyler Wagner
Ecological Applications 30 ( 7)
2
2020
Ecological prediction at macroscales using big data: Does sampling design matter?
Patricia A. Soranno , Kendra Spence Cheruvelil , Boyang Liu , Qi Wang
Ecological Applications 30 ( 6)
1
2020
Restricted spatial regression in practice: Geostatistical models, confounding, and robustness under model misspecification
Ephraim M. Hanks , Erin M. Schliep , Mevin B. Hooten , Jennifer A. Hoeting
Environmetrics 26 ( 4) 243 -254
90
2015
Hierarchical animal movement models for population‐level inference
Mevin B Hooten , Frances E Buderman , Brian M Brost , Ephraim M Hanks
Environmetrics 27 ( 6) 322 -333
39
2016
Predatory Behavior is Primary Predictor of Movement of Wildland-Urban Cougars
Frances E Buderman , Mevin B Hooten , Mat W Alldredge , Ephraim M Hanks
bioRxiv 257295
1
2018
Reconciling multiple data sources to improve accuracy of large-scale prediction of forest disease incidence
Ephraim M. Hanks , Mevin B. Hooten , Fred A. Baker
Ecological Applications 21 ( 4) 1173 -1188
30
2011
Social, spatial, and temporal organization in a complex insect society
Lauren E. Quevillon , Ephraim M. Hanks , Shweta Bansal , David P. Hughes
Scientific Reports 5 ( 1) 13393 -13393
36
2015
A spatially varying stochastic differential equation model for animal movement
James C. Russell , Ephraim M. Hanks , Murali Haran , David Hughes
The Annals of Applied Statistics 12 ( 2) 1312 -1331
11
2018
Animal movement constraints improve resource selection inference in the presence of telemetry error
Brian M. Brost , Mevin B. Hooten , Ephraim M. Hanks , Robert J. Small
Ecology 96 ( 10) 2590 -2597
43
2015