| Gaussian predictive process models for large spatial data sets S Banerjee, AE Gelfand, AO Finley, H Sang Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2008 | 1295 | 2008 |
| Improving the performance of predictive process modeling for large datasets AO Finley, H Sang, S Banerjee, AE Gelfand Computational statistics & data analysis 53 (8), 2873-2884, 2009 | 334 | 2009 |
| Prediction of porosity in metal-based additive manufacturing using spatial Gaussian process models G Tapia, AH Elwany, H Sang Additive Manufacturing 12, 282-290, 2016 | 316 | 2016 |
| A full scale approximation of covariance functions for large spatial data sets H Sang, JZ Huang Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2012 | 293 | 2012 |
| Hierarchical modeling for extreme values observed over space and time H Sang, AE Gelfand Environmental and ecological statistics 16 (3), 407-426, 2009 | 272 | 2009 |
| Hierarchical models facilitate spatial analysis of large data sets: a case study on invasive plant species in the northeastern United States AM Latimer, S Banerjee, H Sang Jr, ES Mosher, JA Silander Jr Ecology letters 12 (2), 144-154, 2009 | 185 | 2009 |
| Continuous spatial process models for spatial extreme values H Sang, AE Gelfand Journal of agricultural, biological, and environmental statistics 15, 49-65, 2010 | 183 | 2010 |
| Composite likelihood for extreme values H Sang Extreme value modeling and risk analysis methods and applications, 2015 | 143* | 2015 |
| On the likelihood function of Gaussian max-stable processes MG Genton, Y Ma, H Sang Biometrika 98 (2), 481-488, 2011 | 139 | 2011 |
| Spatial homogeneity pursuit of regression coefficients for large datasets F Li, H Sang Journal of the American Statistical Association 114 (527), 1050-1062, 2019 | 98 | 2019 |
| Covariance approximation for large multivariate spatial data sets with an application to multiple climate model errors H Sang, M Jun, JZ Huang The Annals of Applied Statistics, 2519-2548, 2011 | 92 | 2011 |
| Beyond the school grounds: Links between density of tree cover in school surroundings and high school academic performance D Li, YC Chiang, H Sang, WC Sullivan Urban forestry & urban greening 38, 42-53, 2019 | 89 | 2019 |
| COVID-19: Short term prediction model using daily incidence data H Zhao, NN Merchant, A McNulty, TA Radcliff, MJ Cote, RSB Fischer, ... PloS one 16 (4), e0250110, 2021 | 67 | 2021 |
| Adaptive Bayesian nonstationary modeling for large spatial datasets using covariance approximations BA Konomi, H Sang, BK Mallick Journal of Computational and Graphical Statistics 23 (3), 802-829, 2014 | 60 | 2014 |
| Work and chronic disease: comparison of cardiometabolic risk markers between truck drivers and the general US population Y Apostolopoulos, MK Lemke, A Hege, S Sönmez, H Sang, DJ Oberlin, ... Journal of Occupational and Environmental Medicine 58 (11), 1098-1105, 2016 | 54 | 2016 |
| Multivariate max-stable spatial processes MG Genton, SA Padoan, H Sang Biometrika 102 (1), 215-230, 2015 | 53 | 2015 |
| Tapered composite likelihood for spatial max-stable models H Sang, MG Genton Spatial Statistics 8, 86-103, 2014 | 48 | 2014 |
| Full-scale approximations of spatio-temporal covariance models for large datasets B Zhang, H Sang, JZ Huang Statistica Sinica, 99-114, 2015 | 44 | 2015 |
| A Bayesian Contiguous Partitioning Method for Learning Clustered Latent Variables. ZT Luo, H Sang, BK Mallick Journal of Machine Learning Research 22, 37:1-37:52, 2021 | 38 | 2021 |
| Nursing home design and COVID-19: Implications for guidelines and regulation X Zhu, H Lee, H Sang, J Muller, H Yang, C Lee, M Ory Journal of the American Medical Directors Association 23 (2), 272-279. e1, 2022 | 32 | 2022 |
alan e gelfandProfessor of Statistical Science Duke UniversityAdresse e-mail validée de stat.duke.edu
