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4. Scalable Predictions for Spatial Probit Linear Mixed Models Using Nearest Neighbor Gaussian Processes. Saha A; Datta A; Banerjee S J Data Sci; 2022; 20(4):533-544. PubMed ID: 37786782 [TBL] [Abstract][Full Text] [Related]
5. NONSEPARABLE DYNAMIC NEAREST NEIGHBOR GAUSSIAN PROCESS MODELS FOR LARGE SPATIO-TEMPORAL DATA WITH AN APPLICATION TO PARTICULATE MATTER ANALYSIS. Datta A; Banerjee S; Finley AO; Hamm NAS; Schaap M Ann Appl Stat; 2016 Sep; 10(3):1286-1316. PubMed ID: 29657659 [TBL] [Abstract][Full Text] [Related]
6. Meta-Kriging: Scalable Bayesian Modeling and Inference for Massive Spatial Datasets. Guhaniyogi R; Banerjee S Technometrics; 2018; 60(4):430-444. PubMed ID: 31007296 [TBL] [Abstract][Full Text] [Related]
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9. Spatial Factor Models for High-Dimensional and Large Spatial Data: An Application in Forest Variable Mapping. Taylor-Rodriguez D; Finley AO; Datta A; Babcock C; Andersen HE; Cook BD; Morton DC; Banerjee S Stat Sin; 2019; 29():1155-1180. PubMed ID: 33311955 [TBL] [Abstract][Full Text] [Related]
10. Bayesian Inference for General Gaussian Graphical Models With Application to Multivariate Lattice Data. Dobra A; Lenkoski A; Rodriguez A J Am Stat Assoc; 2011; 106(496):1418-1433. PubMed ID: 26924867 [TBL] [Abstract][Full Text] [Related]
11. Alternating Gaussian Process Modulated Renewal Processes for Modeling Threshold Exceedances and Durations. Schliep EM; Gelfand AE; Holland DM Stoch Environ Res Risk Assess; 2018 Feb; 32(2):401-417. PubMed ID: 30245582 [TBL] [Abstract][Full Text] [Related]
12. Hierarchical spatial modeling of additive and dominance genetic variance for large spatial trial datasets. Finley AO; Banerjee S; Waldmann P; Ericsson T Biometrics; 2009 Jun; 65(2):441-51. PubMed ID: 18759829 [TBL] [Abstract][Full Text] [Related]
13. Hierarchical Multiresolution Approaches for Dense Point-Level Breast Cancer Treatment Data. Liang S; Banerjee S; Bushhouse S; Finley A; Carlin BP Comput Stat Data Anal; 2008 Jan; 52(5):2650-2668. PubMed ID: 19158942 [TBL] [Abstract][Full Text] [Related]
14. Bayesian Modeling and Analysis of Geostatistical Data. Gelfand AE; Banerjee S Annu Rev Stat Appl; 2017 Mar; 4():245-266. PubMed ID: 29392155 [TBL] [Abstract][Full Text] [Related]
15. Highly Scalable Bayesian Geostatistical Modeling via Meshed Gaussian Processes on Partitioned Domains. Peruzzi M; Banerjee S; Finley AO J Am Stat Assoc; 2022; 117(538):969-982. PubMed ID: 35935897 [TBL] [Abstract][Full Text] [Related]
16. Bayesian spatial models for voxel-wise prostate cancer classification using multi-parametric magnetic resonance imaging data. Jin J; Zhang L; Leng E; Metzger GJ; Koopmeiners JS Stat Med; 2022 Feb; 41(3):483-499. PubMed ID: 34747059 [TBL] [Abstract][Full Text] [Related]
17. Hierarchical Spatial Process Models for Multiple Traits in Large Genetic Trials. Banerjee S; Finley AO; Waldmann P; Ericsson T J Am Stat Assoc; 2010 Jun; 105(490):506-521. PubMed ID: 20676229 [TBL] [Abstract][Full Text] [Related]
18. Practical Bayesian Modeling and Inference for Massive Spatial Datasets On Modest Computing Environments. Zhang L; Datta A; Banerjee S Stat Anal Data Min; 2019 Jun; 12(3):197-209. PubMed ID: 33868538 [TBL] [Abstract][Full Text] [Related]
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20. Gaussian predictive process models for large spatial data sets. Banerjee S; Gelfand AE; Finley AO; Sang H J R Stat Soc Series B Stat Methodol; 2008 Sep; 70(4):825-848. PubMed ID: 19750209 [TBL] [Abstract][Full Text] [Related] [Next] [New Search]