Personal Link

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Contents

• Education
• Research Works
• Casting Project
• Teaching Sections
• Mathematical Statistics Seminar
• How to contact me?

Education

09/1987---07/1991:

Department Mathematics, Peking University
B.S. in Mathematics

09/1991---07/1994:

Department of Probability and Statistics, Peking University
M.S. in Statistics

09/1998---05/2002:

Department of Statistics, University of Michigan
Ph.D. in Statistics

08/2002---:

Department of Statistics, Purdue University
Faculty: Assistant Professor(08/2002--07/2009); Associate Professor (08-2009---)

Research Works

• Research Interests:
• Big Data.
• Spatial Epidemiology.
• Point Processes.
• Geostatistics for large data.
• Problems in restricted parameter.
• Weak Signal Detection.
• Mathematical Statistics.

• Publications:
1. Zhang, T. and Zhang, H.(2017). Non-Fully symmetric space-time matern covariance functions, manuscript.
2. Zhang, T. (2021). Iteratively reweighted least squares with random effects for maximum likelihood in generalized linear mixed effects models.Journal of Statistical Computation and Simulation, DOI: 10.1080/00949655.2021.1928127.
3. Zhang, T. and Yang, B. (2021). Accounting for factor variables in big data regression. Statistica Sinica, 31, 1-28.
4. Zhang, T., and Yang, B. (2021). Online multiple learning with working sufficient statistics for generalized linear models in big data. Statistics and Its Interface, 14, 403-416.
5. Zhang, T. and Lin, G. (2021). Generalized k-means in GLMs with applications to the outbreak of COVID-19 in the United States. Computational Statistics and Data Analysis, DOI: 10.1016/j.cdsa.2021.107217.
6. Huang, H., Liu, X. Zhang, T., and Yang, B. (2020). Sparse orthogonal tensor decomposition for hig-resolution images and videos. AAAI, submited.
7. Wang, J.F., Haining, R., Zhang, T., and Xu, C.D. (2020). Statistics for spatially stratified heterogeneous data. Annals of the American Association of Geographers, revised and resubmitted.
8. Liu, X., Huang, H., Tang, W. Zhang, T. and Yang, B. (2020). Low-rank sparse tensor approximations for high-resolution videos. 19th IEEE Internationa Conference on Machine Learning and Applications (ICLMA2020), accepted.
9. Huang, H., Liu, X., Zhang, T., and Yang, B. (2020). Regression PCA for moving objects separation. In 2020 IEEE Communication Conference (GLONECOM 2020), DOI: 10.1109/GLOBECOM42002.2020.9322471.
10. Lin, G., Zhang, T., Zhang, Y., and Wang, Q. (2020). Statewide staye-at-home directives on the spread of COVID-19 in metropolitan and nonmetropolitan counties in the United States. Journal of Rural Health, DOI: 10.1111/jrh.12464.
11. Zhang, T. and Mateu, J. (2020). Testing first-order spherical symmetry of spatial point processes. Statistica Sinica, 30, 1313-1352.
12. Lin, G. and Zhang, T. (2020). Spatial regression with multiple dependent variables: principal component analysis and spatial autocorrelation. Gepgraphical Analysis, accepted.
13. Zhang, T. (2020). CP decomposition and weighted clique problem. Statistics and Probability Letters, 161, 108723.
14. Liu, X., Tang, Z., Huang, H., Zhang, T., and Yang, B. (2019). Multiple learning for regression in big data. Proceeding of the 18th International Conference on Machine Learning and Applications (ICMLA2019), 587-594. DOI: 10.1109/ICMLA.2019.00109.
15. Lin, G. and Zhang, T. (2020). Spatial regression with multiple dependent variables: principal component analysis and spatial autocorrelation. Gepgraphical Analysis, accepted.
16. Zhang, T. (2020). CP decomposition and weighted clique problem. Statistics and Probability Letters, 161, 108723.
17. Liu, X., Tang, Z., Huang, H., Zhang, T., and Yang, B. (2019). Multiple learning for regression in big data. Proceeding of the 18th International Conference on Machine Learning and Applications (ICMLA2019), 587-594. DOI: 10.1109/ICMLA.2019.00109.
18. Liu, X., Huang, H., Tang, Z., Zhang, T., and Yang, B. (2019). Sparse block regression (SBD) for big data with categorical variables. Proceeding of the 2019 IEEE International Conference on Big Data (BigData2019), 221-226. DOI: 10.1109/BigData47090.2019.9006448.
19. Zhang, T. and Mateu, J. (2019). Substationarity for spatial point processes. Journal of Multivariate Analysis, 171, 22-36.
20. Zhang, T. (2019). General Gaussian estimation, Journal of Multivariate Analysis, 169, 234-247.
21. Chiang, W. Liu, X., Zhang, T., and Yang, B. (2018). A study of exact ridge regression for big data. 2018 IEEE International Conference on Big Data (Big Data), 3821-3830, December 10-13, 2018, Seattlle, Washington.
22. Liu, F., Wang, X., Liu, T., Zhang, T., Yang, B., Hang, Q., Yang, D., and Vian, C. (2018). Towards an AI-driven smart manufacturing discastings. Proceeding of 2018 Die Casting Congress and Exposition, North American Die Casting Association (NADCA). September 15, Indianapolis.
23. Yang, B., Wang, M., Xi, Z, and Zhang, T. (2108) Streaming algorithm for big data logistic regression. 2018 IEEE International Conference on Big Data (Big Data), 2940-2950, December 10-13, 2018, Seattlle, Washington.
24. Liu, Y., Liu, Y., and Zhang, T. (2018). Wald-based spatial scan statistics for cluster detection. Computational Statistics and Data Analysis, 127, 298-310.
25. Zhang, T. and Yang, B.(2018). Dimension reduction for big data. Statistics and Its Interface, 11, 295-306. You can also download it here.
26. Fang, H., Yang, B., and Zhang, T. (2017). Finding the best Box-Cox transformation from massive datasets on Spark. Proceeding of the IEEE International Conference on Big Data(BigData), 2951-2960.
27. Zhang, T. and Huang, Y. (2017). Gradient angle-based analysis for spatiotemporal point processes. Electronic Journal of Statistics, 11, 4424-4451.
28. Gao, Y., Zhang, T., and Yang, B. (2017). Finding the best Box-Cox transformation in big data with meta-model learning: a case study on QCT developer cloud. 2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud), Page 31-34, June 26-28, 2016, New York. DOI: 10.1109/CSCloud.2017.53
29. Zhang, T. and Yang, B.(2017). An exact approach to ridge regression for big data. Computational Statistics, 32, 909-928.
30. Zhang, T. and Yang, B.(2017). Box-Cox transformation in big data. Technometrics, 59, 189-201.
31. Zhang, T.(2017). On independence and separability between points and marks of marked point processes. Statistica Sinica, 27, 207-227.
32. Zhang, T. and Zhuang, R. (2017).Testing proportionality between the first-order intensity functions of spatial point processes, Journal of Multivariate Statistics, 155, 72-82.
33. Zhang, T. and Lin G. (2017). Asymptotic properties of spatial scan statistics under the alternative hypothesis. Bernoulli, 23, 89-109.
34. Zhang. T.(2017). An example of inconsistent MLE of spatial covariance parameters under increasing domain asymptotics. Statistics and Probability Letters, 120, 103-113.
35. Zhang, T. and Yang, B.(2016) Big data dimension reduction using PCA. Proceeding of IEEE International Conference on Smart Cloud (SmartCloud), 152-157, November 18-20, 2016, New York, DOI: 10.1109/SmartCloud.2016.33
36. Yang, B. and Zhang, T. (2016) A scalable feature selection and model updating approach for big data machine learning. Proceeding of IEEE International Conference on Smart Cloud (SmartCloud), 146-151, November 18-20, 2016, New York, DOI: 10.1109/SmartCloud.2016.32
37. Wang, J., Zhang, T., and Fu, B. (2016). A measure of spatial stratified heterogeneity. Ecological Indicators, 67, 250-256.
38. Jin, Z., Zhuang, Q., Dukes J.S., He, J.S., Sokolov, A.P., Chen, M., Zhang, T., Luo, T. (2016). Temporal variability in the thermal requirements for vegetation phenology on the Tibetan plateau and its implications for carbon dynamics. Climatic Change, 138, 617-632.
39. Yang, B. and Zhang, T. (2016). A scalable meta-model for big data security analyses. Proceeding of IEEE Big Data Security on Cloud (BigDataSecurity).
40. Zhang, T. and Lin, G. (2016). On Moran's I coefficient under heterogeneity. Computational Statistics and Data Analysis, 95, 83-94.
41. Zhang, T., Shang, X., and Lin, G. (2015). Spatial parameterization of infant mortality in Anhui Province, China. Spatial Statistics, 14, 286-302.
42. Liu, Y., Pan, Z., Zhuang, Q., Mirallles, D.G., Teuling, A.J., Zhang, T., An, P., Dong, Z., Zhang, J., He, D., Wang, L., Pan, X., Bai, W., Niyogi, D. (2015). Agriculture intensifies soil moisture decline in Northern China. Nature Scientific Reports, 5, article 11261.
43. Zhang, T. (2014). A Kolmogorov-Smirnov type test for independence between marks and points of marked point processes. Electronic Jounral of Statistics, 8, 2557-2584. DOI: 10.1214/14-EJS961.
44. Zhang, T., and Zhou, B. (2014). Test for the first-order stationarity for spatial point processes in arbitrary regions. Journal of Agricultural, Biological and Environmental Statistics, 19, 387-404.
45. Zhang, T. and Lin G. (2014). Family of power divergence spatial scan statistics. Computational Statistics and Data Analysis, 75, 162-178.
46. Zhang, T. and Zhuang, Q. (2014). On the local odds ratio between points and marks in marked point processes. Spatial Statistics, 9, 20-37.
47. Zhang, T.(2014). Spatial scan statistics. Enclyclopedia of Environmentrics, 5, DOI: 10.1002/9781118445112.stat07640..
48. Zhang, T. and Lin, G. (2013). On the limiting distribution of the spatial scan statistic. Journal of Multivariate Analysis, 122, 215-225.
49. Bowen, G.J., West, J., Zhao, L., Takahashi, G., Miller, C., and Zhang, T. (2012). Cyberinfrastructure for isotope analysis and modeling. Eos, Transactions American Geophucal Union, 93, 185-187.
50. Lin, G. and Zhang, T. (2012). Examining extreme weather effects on birth weight from the individual effect to spatiotemporal aggregation effects. Journal of Agriculatural, Biological, and Environmental Statistics, 17, 490-507.
51. Zhang, T., Zhang, Z., and Lin, G. (2012). Spatial scan statistics with overdispersion. Statistics in Medicine, 31, 762--774.
52. Bowen, G.J., Kennedy, C.D., Henne, P.D., Zhang, T. (2012). Footprint of recycled water subsidies downwind of Lake Michigan. Ecoshpere, 3, Article 53.
53. Wan, H., Zhang, T., and Zhu, Y. (2012). Detection and localization of hidden radioactive sources with spatial statistical method.. Annals of Operation Research, 192, 87-104.
54. Zhang, T. (2012). Radioactive target detection with wireless sensor network. Computer, Informatics, Cybernetics and Applications. Lecture Notes in Electrical Engineering, 107, 293-301.
55. Zhang, T. (2011). Statistical approach to radioactive target detection and location via wireless sensor networks. IEEE Proceeding of 2011 Third International Conference on Signal Processing Systems (ICSPS 2011), August 27-28, Yantai, China.
56. Zhang, T. (2011). Detecting and locating radioactive signals with wireless sensor networks. ISIC, 2011, 110-113. IEEE ISIC '11 Proceedings of the 2011 International Conference on Information Security and Intelligence Control, Page 110-103. doi: 10.1109/ISIC.2011.26.
57. Lee, H., Zhao, L., Bowen, G.J., Miller, C.C, Ajay, K., Zhang, T., and West, J. (2011). Enabling online geospatial isotopic model development and analysis. IEEE TereGrid Conference: Extreme Digital Discovery, Salt Lake City, July 18-21, 2011. Doi: 10.1145/2016741.2016783.
58. Zhang, T. (2010). Spatial disease surveillance: methods and applications. Frontiers in Computational and Systems Biology (ed. by Feng, J. and Sun, F.), 238-300.
59. Zhang, T. and Lin, G. (2009). Spatial scan statistics in loglinear models. Computational Statistics and Data Analysis, 53, 2851-2858.
60. Zhang, T. and Lin, G. (2009). Cluster detection based on spatial associations and iterated residuals in generalized linear mixed models. Biometrics, 65,353-360.
61. Zhuang, Q., Zhang, T., Xiao, J. and Luo, T. (2009). Quantification of net primary production of Chinese forest ecosystems with spatial statistical approaches. Mitigation and Adaptation Strategies for Global Change, 14, 85-99.
62. Zhang, T. (2008). A limiting distribution of G statistics. Statistics and Probability Letters, 78, 1656-1661.
63. Zhang, T. and Lin, G. (2008). Identification of local clusters for count data: a model-based Moran's I test. Journal of Applied Statistics, 35, 293-306.
64. Zhang, T. and Lin, G. (2007). A decomposition of Moran's I for clustering detection. Computational Statistics and Data Analysis, 51, 6123-6137.
65. Lin, G. and Zhang, T. (2007). Loglinear residual tests of Moran'I autocorrelation: an application to Kentucky breast cancer incidence data. Geographical Analysis, 39, 293-310.
66. DasGupta, A. and Zhang, T. (2006). On the false discovery rates of a frequentist: asymptotic expansions. IMS Lecture Notes Monograph, 50, 190-212.
67. Zhang, T. and Lin, G. (2006). A supplemental indicator of high-value or low-value spatial clustering . Geographical Analysis, 38, 209-225.
68. Zhang, T. (2006). A modification for Bayesian credible intervals. Communication in Statistics, 35, 1703-1711.
69. Zhang, T. (2006). Existence of the signal in the signal plus background model. IMS Lecture Notes Monograph, 50, 144-155.
70. DasGupta, A. and Zhang, T. (2005). Inference for binomial and multinomial parameters. As a chapter of the book Encyclopedia of Statistical Sciences, 2nd edition, edited by Drs. N. Balakrishman and Campbell B. Read and Brani Vidakovic, published by Wiley.
71. Zhang. T. and Woodroofe, M. (2005). Admissible minimax prediction of the signal with known background. Statistica Sinica, 15, 59-72.
72. Lin, G. and Zhang, T. (2004). A method for testing low-value spatial clustering for rare diseases . Acta Tropica, 91, 279-289.
73. Zhang, T. and Woodroofe. M. (2003). Credible and confidence sets for restricted parameter spaces. Journal of Statistical Planning and Inference, 115, Page 479-490.
74. Zhang. T. and Woodroofe, M. (2002). Credible and confidence sets for the ratio of variance components in the balanced one-way model . Sankhya, 64, Page 545-560.
75. Woodroofe, M. and Zhang, T. (2002). Discussion of ``Setting confidence intervals for bounded parameters'', by Mark Mandelkern. Statistical Science, 17, Page 168-171.

Project