Ganggang Xu

Ganggang Xu

Assistant Professor, University of Miami

Research Grants

  1. NSF SES-1902195: 2018-2021.
  2. Collaboration Grants for Mathematicians (Award ID: 524205), Simons Foundation: 2017-2018.

Statistical Methodology/Machine Learning

  1. Jalilian, A., Cuevas-Pacheco, F., Xu, G., and Waagepetersen, R. (2024+) “Composite likelihood inference for space-time point processes”, submitted.
  2. Lu, C., Guan, Y., van Lieshout, MC., and Xu, G.. (2024+) “XGBoostPP: Tree-based estimation of point process intensity functions”, submitted.
  3. Jalilian, A., Poinas, A., Xu, G., and Waagepetersen, R. (2024+) “A central limit theorem for a sequence of conditionally centered and -mixing random fields”, under revision.
  4. Li, K., Liu, R., Xu, G., and Shang, Z. (2024) “Nonparametric Inference under B-bits Quantization”, Journal of Machine Learning Research, 25(19), 1−68.
  5. Fang, G.(∗), Xu, G.(∗), Xu, H., Zhu, X., Guan, Y. (2023) “Group Network Hawkes Process”, Journal of the American Statistical Association, Theory & Method, in press. (∗: joint first authors with equal contributions.)
  6. Zhu, X.(∗), Xu, G.(∗), Fan, J. (2023) “Simultaneous Estimation and Group Identification for Network Vector Autoregressive Model with Heterogeneous Nodes”, Journal of Econometrics, 105564. (∗: joint first authors with equal contributions.)
  7. Liu, R., Xu, G.(∗), and Shang, Z. (2023) “Distributed Adaptive Nearest Neighbor Classifier: Algorithm and Theory”, Statistics and Computing, 33(96).
  8. Xu, G., Zhang, J., Li, Y., and Guan, Y. (2022) “Bias-correction and Test for Mark-point Dependence with Replicated Marked Point Processes.” Journal of the American Statistical Association, Theory & Method, in press.
  9. Chu, T., Guan, Y., Waagepetersen, R., and Xu, G. (2022) “Quasi-Likelihood for Multivariate Spatial Point Processes with Semiparametric Intensity Functions.” Spatial Statistics, 100605.
  10. Xu, G., Liang, C.(#), Waagepetersen, R., and Guan, Y. (2022) “Semi-parametric Goodness-of-fit Test for Clustered Point Processes with a Shape-constrained Pair Correlation Function.” Journal of the American Statistical Association, Theory & Method, accepted. (#: Ph.D. student supervised)
  11. Zhang, J., Cai, B., Zhu, X., Wang, H., Xu, G., Guan, Y. (2022) “Learning Human Activity Patterns using Clustered Point Processes with Active and Inactive States”, Journal of Business & Economic Statistics, online.
  12. Hessellund, K. B., Xu, G., Guan, Y., and Waagepetersen, R. (2022) “Secondorder Semi-parametric Inference for Multivariate Log Gaussian Cox Processes.” Journal of the Royal Statistical Society, Series C, 71(1), 244– 268.
  13. Yin, L., Xu, G., Sang, H., and Guan, Y. (2021) “Row-clustering of a Point Process-valued Matrix.” Advances in Neural Information Processing Systems (NeurIPS), 34.
  14. Hessellund, K. B.(∗), Xu, G.(∗), Guan, Y., and Waagepetersen, R. (2021) “Semiparametric Multinomial Logistic Regression for Multivariate Point Pattern Data.” Journal of the American Statistical Association, Theory & Method, 1-16. (∗: joint first authors with equal contributions.)
  15. Xu, G., Wang, M., Bian, J., Burch, T. R., Andrade, S. C., Huang, H., Zhang, J., Guan, Y. (2020) “Semi-parametric Learning of Structured Temporal Point Processes.” Journal of Machine Learning Research, 21(192), 1-39.
  16. Xu, G., Zhao, C., Jalilian, A., Waagepetersen, R., Zhang, J., Guan, Y. (2020) “Nonparametric Estimation of the Pair Correlation Function of Replicated Inhomogeneous Point Processes.” Electronic Journal of Statistics, 14, 3730-3765.
  17. Xu, G., Zhu, H., Lee, J. J. (2020) “Borrowing Strength and Borrowing Index for Bayesian Hierarchical Models.” Computational Statistics & Data Analysis, 144, 106901.
  18. Xu, G., Shang, Z., Cheng, G. (2019) “Distributed Generalized Cross-Validation for Divide-and-Conquer Kernel Ridge Regression and its Asymptotic Optimality.” Journal of Computational and Graphical Statistics, 28, 891-908.
  19. Xu, G., Waagepetersen, R., Guan, Y. (2019) “Stochastic Quasi-likelihood for Case-Control Point Pattern Data.” Journal of the American Statistical Association, Theory & Method, 114, 631-644.
  20. Xu, G., Shang, Z., Cheng, G. (2018) “Optimal Tuning Parameter Selection for the Divide-and-conquer Kernel Ridge Regression with Massive Data.” Proceedings of the 35th International Conference on Machine Learning (ICML, Oral) 80, 5483-5491.
  21. Xu, G., Genton, M. (2017) “Tukey’s g-and-h Random Fields.” Journal of the American Statistical Association, Theory & Method, 112, 1236-1249.
  22. Xu, G., Genton, M. (2016) “Tukey Max-Stable Processes for Spatial Extremes.” Spatial Statistics, 18, 431-443.
  23. Xu, G., Genton, M. (2015) “Efficient Maximum Approximated Likelihood Inference for Tukey’s g-and-h Distribution.” Computational Statistics & Data Analysis, 91, 78-91.
  24. Xu, G., Liang, F., Genton, M.G. (2015) “A Bayesian Spatio-temporal Geostatistical Model with an Auxiliary Lattice for Large Datasets.” Statistica Sinica, 25, 61-79.
  25. Xu, G., Wang, S., Huang, J.Z. (2014) “Focused Information Criterion and Model Averaging Based on Weighted Composite Quantile Regression.” Scandinavian Journal of Statistics, 41, 365-381.
  26. Xu, G., Huang, J.Z. (2012) “Asymptotic Optimality and Efficient Computation of the Leave-subject-out Cross-validation.” Annals of Statistics. 40, 3003-3030.
  27. Xu, G., Xiang, Y.B., Wang, S. and Lin, Z.Y. (2012) “Regularization and Variable Selection for Infinite Variance Autoregressive Models.” Journal of Statistical Planning and Inference. 142, 2545-2553.
  28. Xu, G. and Wang, S. (2011) “A Goodness-of-fit Test of Logistic Regression Based on Case-control Data with Measurement errors.” Biometrika. 98, 877-886.
  29. Zhang, G., Xia, Y. and Xu, G. (2006), “Instantaneous Availability Assessment of Renewable Component in Exponential Distributions.” Appl. Math. J. Chinese Univ. Ser. B, 2006, 21(4): 397-404.

Interdisciplinary Collaboration

  1. Chen, X., Lin, L., et al., Xu, G., Song, Y., Xue, Y., Duan, Q. (2020) “Histogram analysis in predicting the grade and histological subtype of meningiomas based on diffusion kurtosis imaging.” Acta Radiologica, 61(9), 1228-1239.
  2. Hathout, Y., Liang, C., Ogundele, M., Xu, G., et al. (2019) “Disease-specific and glucocorticoid-responsive serum biomarkers for Duchenne Muscular Dystrophy.” Scientific reports, 9, 1-13.
  3. Zhao, H., Wang, B., Xu, G., Dong, Y., Dong, Q., Cao, W. (2019) “Collateral grade of the Willis’ circle predicts outcomes of acute intracranial internal carotid artery occlusion before thrombectomy.” Brain and behavior, 9, e01452.
  4. Lin, L., Xue, Y., et al., Xu, G., Geng, D., Zhang, J. (2019) “Grading meningiomas using mono-exponential, bi-exponential and stretched exponential model-based diffusion-weighted MR imaging.” Clinical radiology, 74, 651.e15-651.e23.
  5. Deng, C., Lin, W., Ye, X., Li, Z., Zhang, Z., Xu, G. (2018) “Social Media Data as a Proxy for Hourly Fine-scale Electric Power Consumption Estimation.” Environment and Planning A: Economy and Space, 50, 1553-1557.
  6. Lin, L., Chen, X., et al., Xu, G., Duan, Q., Xue, Y. (2018) “Differentiation between vestibular schwannomas and meningiomas with atypical appearance using diffusion kurtosis imaging and three-dimensional arterial spin labeling imaging.” European journal of radiology, 109, 13-18.