| A Bayesian spatial–temporal model with latent multivariate log‐gamma random effects with application to earthquake magnitudes G Hu, J Bradley Stat 7 (1), e179, 2018 | 26 | 2018 |
| Bayesian spatial homogeneity pursuit of functional data: an application to the us income distribution G Hu, J Geng, Y Xue, H Sang Bayesian Analysis 18 (2), 579-605, 2023 | 23 | 2023 |
| Geographically weighted regression analysis for spatial economics data: A Bayesian recourse Z Ma, Y Xue, G Hu International Regional Science Review 44 (5), 582-604, 2021 | 22 | 2021 |
| Geographically weighted Cox regression for prostate cancer survival data in Louisiana Y Xue, ED Schifano, G Hu Geographical Analysis 52 (4), 570-587, 2020 | 20 | 2020 |
| Bayesian group learning for shot selection of professional basketball players G Hu, HC Yang, Y Xue Stat 10 (1), e324, 2021 | 19 | 2021 |
| Bayesian nonparametric nonhomogeneous Poisson process with applications to USGS earthquake data J Geng, W Shi, G Hu Spatial Statistics 41, 100495, 2021 | 19 | 2021 |
| Bayesian Variable Selection for Pareto Regression Models with Latent Multivariate Log Gamma Process with Applications to Earthquake Magnitudes HC Yang, G Hu, MH Chen Geosciences 9 (4), 2019 | 17 | 2019 |
| Benchmarking cell-type clustering methods for spatially resolved transcriptomics data A Cheng, G Hu, WV Li Briefings in bioinformatics 24 (1), bbac475, 2023 | 16 | 2023 |
| Time fused coefficient SIR model with application to COVID-19 epidemic in the United States HC Yang, Y Xue, Y Pan, Q Liu, G Hu Journal of Applied Statistics 50 (11-12), 2373-2387, 2023 | 15 | 2023 |
| Spatial Clustering Regression of Count Value Data via Bayesian Mixture of Finite Mixtures P Zhao, HC Yang, DK Dey, G Hu Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and …, 2023 | 15* | 2023 |
| Heterogeneous regression models for clusters of spatial dependent data Z Ma, Y Xue, G Hu Spatial Economic Analysis 15 (4), 459-475, 2020 | 15* | 2020 |
| Heterogeneity learning for SIRS model: an application to the COVID-19 G Hu, J Geng arXiv preprint arXiv:2007.08047, 2020 | 15 | 2020 |
| Analysis of professional basketball field goal attempts via a Bayesian matrix clustering approach F Yin, G Hu, W Shen Journal of Computational and Graphical Statistics 32 (1), 49-60, 2023 | 14 | 2023 |
| A Bayesian marked spatial point processes model for basketball shot chart J Jiao, G Hu, J Yan Journal of Quantitative Analysis in Sports 17 (2), 77-90, 2021 | 11 | 2021 |
| Modified Kaplan–Meier Estimator and Nelson–Aalen Estimator with Geographical Weighting for Survival Data G Hu, F Huffer Geographical Analysis, 2019 | 10 | 2019 |
| Zero‐inflated Poisson model with clustered regression coefficients: Application to heterogeneity learning of field goal attempts of professional basketball players G Hu, HC Yang, Y Xue, DK Dey Canadian Journal of Statistics 51 (1), 157-172, 2023 | 8 | 2023 |
| Bayesian nonparametric learning for point processes with spatial homogeneity: A spatial analysis of NBA shot locations F Yin, J Jiao, J Yan, G Hu International Conference on Machine Learning, 25523-25551, 2022 | 8* | 2022 |
| Most likely optimal subsampled Markov chain Monte Carlo G Hu, H Wang Journal of Systems Science and Complexity 34 (3), 1121-1134, 2021 | 8 | 2021 |
| A comparison of Monte Carlo methods for computing marginal likelihoods of item response theory models Y Liu, G Hu, L Cao, X Wang, C Ming-Hui Journal of the Korean Statistical Society, 2019 | 8 | 2019 |
| Heterogeneity pursuit for spatial point pattern with application to tree locations: A Bayesian semiparametric recourse J Jiao, G Hu, J Yan Environmetrics 32 (7), e2694, 2021 | 7 | 2021 |
Yishu XueGoogleVerified email at google.com
