Huiyan Sang
Huiyan Sang
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Cited by
Cited by
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
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
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
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
Hierarchical modeling for extreme values observed over space and time
H Sang, AE Gelfand
Environmental and ecological statistics 16 (3), 407-426, 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
Continuous spatial process models for spatial extreme values
H Sang, AE Gelfand
Journal of agricultural, biological, and environmental statistics 15, 49-65, 2010
On the likelihood function of Gaussian max-stable processes
MG Genton, Y Ma, H Sang
Biometrika 98 (2), 481-488, 2011
Composite likelihood for extreme values
H Sang
Extreme value modeling and risk analysis methods and applications, 2015
Spatial homogeneity pursuit of regression coefficients for large datasets
F Li, H Sang
Journal of the American Statistical Association 114 (527), 1050-1062, 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
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
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
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
Multivariate max-stable spatial processes
MG Genton, SA Padoan, H Sang
Biometrika 102 (1), 215-230, 2015
Tapered composite likelihood for spatial max-stable models
H Sang, MG Genton
Spatial Statistics 8, 86-103, 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
Full-scale approximations of spatio-temporal covariance models for large datasets
B Zhang, H Sang, JZ Huang
Statistica Sinica, 99-114, 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
Quantitative evaluation of key geological controls on regional Eagle Ford shale production using spatial statistics
Y Tian, WB Ayers, H Sang, WD McCain Jr, C Ehlig-Economides
SPE Reservoir Evaluation & Engineering 21 (02), 238-256, 2018
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