International Conferences

2023

• Keynote Speaker (fully funded):  International symposium on new developments of theories and methodologies for large complex data.  Dec 07-09, 2023, The University of Tsukuba, Japan.
• Invited speaker:         The  2023 ICSA-China conference  held on  June 30- July 03 2023 in Chengdu, China.                
• Invited speaker: The Ecosta 2023 held on August 01-03,  Tokyo, Japan.

          2021

• Special Invited Speaker (fully funded): International symposium on new developments of theories and methodologies for large complex data.  Nov 5-6, 2021, The University of Tsukuba, Japan.
• Conference President (fully funded): The 2nd International Conference on Education, Knowledge and Information Management (ICEKIM 2021), Jan 29-31 2021, Xiamen, China.

       2020

• Keynote Speaker (fully funded): The 3rd Int’l Conference on Statistics, Mathematical Modelling and Analysis (SMMA  2020), Nov. 6-8, 2020, Xiamen, China (Online meeting via ZOOM).
• Keynote Speaker (fully funded):  2020 International Conference on Big Data and Information Education.  April 24-26, 2020, Zhangjiajie, China. (Online meeting via ZOOM).

2019

• Keynote Speaker, (partially funded) . The 2019 Forum of the Theory, Method, and Application Frontier of Statistics. July 15-17, 2019. Lanzhou, China.

• Invited Speaker (partially funded). The 2019 ICSA-China conference held on July 1- 4 2019 in Nankai University at Tianjin, China.
• Invited Speaker. The 2019 ICSA International Conference held on December 20-22, 2019 at Zhejiang University, Zhejiang, China.

• Invited Speaker. The 3rd International Conference on Econometrics and Statistics (EcoSta 2019) held on June 25-27, 2019 at National Chung Hsing University , Taichung, Taiwan.
• Invited Speaker (partially funded). The 2019 ICSA-China conference held on July 1- 4 2019 in Nankai University at Tianjin, China.
• Invited Speaker. The 2019 ICSA International Conference held on December 20-22, 2019 at Zhejiang University, Zhejiang, China.

2018

• Invited Speaker. The International Conference on Frontiers of Data Science held on May 18-20, 2018. Hangzhou, China.
• Invited Speaker. The 5th IMS Asia Pacific Region Meeting, , June 26-29, 2018, NUS, Singapore.
• Invited Speaker. The International Conference on Functional Data Analysis, December 8-9, 2018, University of Melbourne, Australia.
• Invited Speaker. The CFE-CMStatistics Conference, December 14-16, 2018, Pisa, Italy.
2017

• Invited Speaker. The Melbourne-Singapore Probability and Statistics Forum. December 25-28, 2017. University of Melbourne, Australia.
• Invited Speaker. The 1st International on Econometrics and Statistic. June 15-17, 2017. Hong Kong University of Science and Technology, Hong Kong, China.
• Invited Speaker. The 2017 IMS-China International Conference on Statistics and Probability” held on June 28—July 2, 2017 at Guangxi University for Nationalities in Nanning, Guangxi, China.
2016

• Invited Speaker. The 2016 ICSA Conference on Data Science. Dali, Yunnan, China. July 1-4, 2016.
• Invited Speaker. The 4th IMS Asia Pacific Region Meeting, Hong Kong, China, June 27-30, 2016.
• Invited Speaker. The Symposium of Fronitiers of Statistics and Data Sciences. Hong Kong Polytechnic University, Hong Kong, China, June 25-26, 2016.
• Invited Speaker. The 2016 ICSA Applied Statistics Symposium. Atlanta, USA. June 12-15, 2016.
2015
• Invited Speaker. The 24th South Taiwan Statistics Conference and 2015 Chinese Institute of Probability and Statistics Annual Meeting. June 27-28, 2015. Changhua, Taiwan.
• Invited Speaker. The Statistical Computing Asia 2015, July 1- 2, 2015. Taipei, Taiwan.
2014
• Invited Speaker. The 3rd IMS-APRM conference. June 29-July 3, 2014. Taipei, Taiwan.
• Invited Speaker. The ASC-IMS Annual Meeting. July 7-10, 2014. Sydney, Australia.
• Invited Speaker. The ISI-Regional Statistics Conference. Nov. 16-19, 2014. Kuala Lumpur, Malaysia.
• Invited Speaker. The IASSL-International Conference. Dec. 28-30, 2014. Colombo, Sri Lanka.
2013
• Invited Speaker. 6th International Conference of the ERCIM WG on
Computational and Methodological Statistics (ERCIM 2013)
14-16 December 2013, Senate House, University of London, UK
• Invited Speaker. IMS-SWUFE International Conference on Statistics and Probability. July 1-3, 2013. Chengdu, China.
•
2011-2012
• Invited Speaker. The 2nd IMS-APRM conference, July 2-4, 2012. Tsukuba, Japan.
• Invited Plenary Speaker. The ISI Satellite Meeting, August 17-19, 2011. Copenhagen, Denmark.
2009-2010
• Invited Speaker. The 8th ICSA International Conference. Dec 19-20, 2010. Guangzhou, China.
• Invited Speaker. International conference on frontiers of interface between statistics and sciences. Dec. 30, 2009-Jan 2, 2010. Hyderabad, India.
2008
• Invited Speaker. The 7th world congress in probability and statistics. July 14-19, 2008. Singapore.
• Invited Speaker. Workshop on high-dimensional data analysis. Feb 27-29, 2008. Singapore.
2007
• Invited Speaker. The 2007 Fall Conference of the Korea Statistical Society. November 9-10, 2007. Seoul, South Korea.
• Invited Speaker. The 2007 International Conference on the Frontiers of Statistics” , August, 13-15, 2007, Kunming, Yunnan, China.
• Invited Speaker. Taipei International Statistical Symposium and ICSA International Conference , June 25-28, 2007. Taipei, Taiwan, ROC.
• Invited Speaker . The 15-th International Conference of Forum for Interdisciplinary Mathematics on Interdisciplinary Mathematical & Statistical Techniques. May 20-23, 2007. Shanghai, China.
2006
• Participant. Workshop on Frontiers of Statistics. May 18-20, 2006. Princeton Univ., USA.
• Invited Speaker. Taipei Statistical Workshop. Dec 13-15, 2006. Taipei, ROC.
• Invited Speaker. Biostatistics Workshop-CTERU, August 18, 2006, USA.
2002-2004
• Invited Speaker. The 6th International ICSA Conference. July 21-23, 2004, National University of Singapore, Singapore.
• Invited Speaker. The Emerging Issues in Longitudinal Analysis. July 27-Aug. 2, 2002. Mount Holyoke College, MA, USA.

2001
• Invited Speaker. IMS New Researchers’ Conference. July 31-August 3, 2001. Atlanta, Georgia, USA.
• Invited Speaker. The 5th ICSA International Conference. August 17-19, 2001 Hong Kong, China.
2000
• Invited Speaker. The ICSA Applied Statistics Symposium. June 1-3, 2000. Piscataway, New Jersey, USA.
• Invited Speaker. The ENAR Spring Meeting. Mar 19-23, 2000. Chicago, Illinois, USA.

PhD Dissertation

 Zhang, J. T. (1999). Smooth functional data analysis. Ph. D. Dissertation. Department of Statistics, UNC-Chapel Hill, USA.

Master Thesis

 Zhang, J. T. (1991). Experimental design of Latin-square type. Institute of Applied Mathematics, Academia Sinica. Beijing China.

• Zhang, J. T. (2013). Smoothing and semiparametric models. The SAGE Handbook of Multilevel Modeling. M. A. Scott, J. S. Simonoff, and B. D. Marx (Eds.). SAGE Publications Ltd.
• Fan, J. and Zhang, J. T. (2007). A note on the bounded normal mean problem. in Festschrift for Kjell Doksum, World Scentific Publishing Co. Ltd. 635-648.
• Zhang, J. T. (2005). Order-dependent thresholding with applications to regression splines. Contemporary Multivariate Analysis and Experimental Designs. J. Fan and Gang Li (Eds.). pp. Spring-Verlag, New York.
• Chen, J. W., Zhang, J. T. and Wu, H. (2005). Discretization approach and nonparametric modeling for long-term HIV dynamic model. Computational Science and Its Applications. O. Gervasi et al. (Eds.). pp. 519-527. Spring-Verlag, Berlin.
• Zhu, L., Fang, K. T. and Zhang, J. T. (1995)., A project NT-type test for spherical symmetry of a multivariate distribution. Multivar. Statist. and Matrices in Statist.,eds. E.-M. Tiit, T. Kollo and H. Niemi, VSP-TEV, the Netherlands: Utrecht, 109-122.

2024

• Qiu, Z., Fan, J. , Zhang, J.-T. and Chen, J. (2024).  Test for equality of several covariance matrix functions for multivariate functional data. J of Multivariate Analysis.  Vol 199, 105243.
• Zhu, T., Zhang, J.-T., and Cheng, M.-Y. (2024). A global test for heteroscedastic one-way FMANOVA with applications.  Journal of Multivariate Analysis. Vol 231, 106133.

    2023

• Zhang, L., Zhu, T. and Zhang, J.-T. (2023).  Two-sample Behrens-Fisher problems for high-dimensional data: a normal reference scale-invariant test. Journal of Applied Statistics, 50 (3), 456-476.
• Zhu, T. , Wang, P. and Zhang, J.-T. (2023). Two-sample Behrens-Fisher problems for high-dimensional data: a normal-reference F-type test.  Computational Statistics.
• Ong, Z., Chen, A., Zhu, T. and Zhang, J.-T. (2023). Testing equality of several distributions in high dimension: a MMD-based approach. Mathematics 2023, 11(20), 4374; https://doi.org/10.3390/math11204374.

      2022

• Zhang, J.-T., Guo, J. and Zhou, B. (2022). Testing equality of several distributions in separable metric spaces: A maximum mean discrepancy based approach. of Econometrics.  https://doi.org/10.1016/j.jeconom.2022.03.007.
• Zhang, J.-T. and Zhu, T. (2022a). A new normal reference test for linear hypothesis testing in high-dimensional heteroscedastic one-way MANOVA.  Computational Statistics & Data Analysis.
• Zhang, J.-T. and Zhu, T. (2022b). A Further Study on Chen–Qin’s Test for Two-Sample Behrens–Fisher Problems for High-Dimensional Data. Journal of Statistical Theory and Practice. 16(1), 1-32.
• Zhang, J.-T. and Zhu, T. (2022c). A revisit to Bai–Saranadasa’s two-sample test. Journal of Nonparametric Statistics.34(1), 58-76.
• Zhu, T. and Zhang, J.-T. (2022a). A new $ k $-nearest neighbors classifier for functional data. Statistics and Its Interface. 15(2), 247-260.
• Zhu, T. and Zhang, J.-T. (2022b). Linear hypothesis testing in high-dimensional one-way MANOVA: a new normal reference approach. Computational Statistics. 37(1), 1-27.
• Zhang, J. T. and Smaga, L. (2022). Two-sample tests for equal distributions in separable metric space: ew maximum mean discrepancy based approaches. Electronic Journal of Statistics, 16(2), 4090-4132.
• Zhang, J.-T., Zhou, B. and Guo, J. (2022). Linear hypothesis testing in high-dimensional heteroscedastic one-way MANOVA: A normal reference L2-norm based test. Journal of Multivariate Analysis. 187. 104816.
• Guo, J., Zhang, J.-T. and Zhou, B. (2022). Discussion of “Estimation of Hilbertian varying coefficient models”.  Statistics and Its Interface. 15(2), 153-154.

                                                              2021

• Qiu, Z., Chen, J. and Zhang J-T. (2021).  Two-sample tests for multivariate functional data with applications. Computational Statistics and Data Analysis. 157 (12)
• Zhu T. and Zhang, J.-T. (2021). A new k-nearest neighbors classifier for functional data. Statistics and Its Interface.
• Zhang, J.-T., Zhou, B., Guo, J. and Zhu, T. (2021). Two-sample Behrens-Fisher problems for high-dimensional data: a normal reference approach. Journal of Statistical Planning and Inference 213, 142-161.
• Zhang, J.-T., Zhou, B. and Guo, J. (2021). Linear hypothesis testing in high-dimensional heteroscedastic one-way MANOVA: a normal-reference L2-norm based test.  Journal of Multivariate Analysis.  187, 104816.
• Zhu, T. and Zhang, J.-T. (2021). Linear hypothesis testing in high-dimensional one-way MANOVA: a new normal-reference test.   Computational Statistics, 1-27.

2020

• Zhang, J. -T., Guo, J., Zhou, B., and Cheng, M.- Y. (2020). A simple two-sample test in high-dimensions based on L2-norm. Journal of American Statistical Association. 115(530), 1011-1027.
• Smaga, L. and Zhang, J.-T. (2020). Linear hypothesis testing for weighted functional data with applications. Scandinavian Journal of Statistics, 47(2), 493-515.
• Zhang, L., Zhu, T.-M. and Zhang, J.-T. (2020). A Simple Scale-Invariant Two-Sample Test for High-dimensional Data. Journal of Econometrics and Statistics. 14, 131-144.
• Zhu, T.-M. and Zhang, J.-T. (2020). Cosine similarity-based classifiers for functional data. “ Contemporary Experimental Design, Multivariate Analysis and Data Mining.” 277-292.

2019

• Guo, J., Zhou, B. and Zhang, J.-T. (2019) New Tests for Equality of Several Covariance Functions for Functional Data. Journal of American Statistical Association. Vol 114, No. 527, 1251-1263.
• Guo, J. Zhou, B. and Zhang, J.-T. (2019). An L2-norm based test for equality of several covariance functions: a further study. Test, Vol 28, No. 4, 1092-1112.
• Smaga, L. and Zhang, J.-T. (2019). Linear hypothesis testing with functional data. Technometrics. 61(1):99-110.
• Zhang, J.-T., Cheng, M. Y., Wu, H. T. and Zhou, B. (2019). A new test for functional one-way ANOVA with applications to ischemic heart screening. Computational Statistics and Data Analysis. 132:3-17.
• Zhou, B., Guo, J., Chen, J.-W. and Zhang, J.-T. (2019). An adaptive spatial-sign-based test for mean vectors of elliptically distributed high-dimensional data. Statistics and Its Interface. 12(1):93-106. DOI: 10.4310/SII.2019.v12.n1.a9.

2017-2018

• Guo, J. , Zhou, B. and Zhang, J.-T. (2018). Testing the equality of several covariance functions for functional data: A supremum-norm based test. Computational Statistics and Data Analysis 124:15-26.
• Zhang, J.- T., Guo, J., and Zhou, B. (2017). Linear hypothesis testing in high-dimensional one-way MANOVA. J. of Multivariate Analysis. 155: 200-216.
• Zhou, B., Guo, J. and Zhang, J.- T. (2017). High-Dimensional General Linear Hypothesis Testing under Heteroscedasticity. J. of Statistical Planning and Inferences. 188: 36-54.

2016

• Cheng，MY，Honda， T. and Zhang, J-T (2016). Forward variable selection for sparse ultra-high dimensional varying coefficient modes. J. of American statistical association, Vol 111, No. 515, 1209-1221.
• Zhang, J. T., Zhou, B., Guo, J. and Liu, X. (2016). A modified Bartlett test for heteroscedastic two-way MANOVA. J. of Advanced Statistics. p. 94-108, 1(2).
• Xuefeng Liu, Jia Guo, Bu Zhou, Jin-Ting Zhang (2016) . Two Simple Tests for Heteroscedastic Two-Way ANOVA. Statistics Research Letters, 5(0):6-16. doi: 10.14355/srl.2016.05.002.

2013-2015
• Xiao, S. and Zhang, J.-T. (2015). Modified tests for heteroscedastic two-way MANOVA. J. of Advanced Statistics. p. 1-16, 1(1).
• Zhang, J.-T. and Liang, X. (2014), One-way ANOVA for functional data via globalizing the pointwise F-test. Scandinavian Journal of Statistics, 41: 51–71. doi: 10.1111/sjos.12025
• Zhang, J. T. and Liu, X. (2013). A modified Bartlett test for heteroscedastic one-way MANOVA. Metrika, 76, 135-152. DOI 10.1007/s00184-011-0379-z.

2012

• Zhang, J. T. (2012b). An approximate degrees of freedom test for heteroscedastic two-way ANOVA. J. of Statist. Plan. Infer., 142, 336-346.
• Zhang, J. T. (2012a). An approximate Hotelling T-square test for heteroscedastic one-way ANOVA. Open J. Statist. 2, 1-11.
• Zhang, J. T. and Liu X. (2012). A modified Bartlett test for linear hypothesis in heterocedastic one-way ANOVA”. Statistics and its interface. 5, 253-262.
• Zhang, J. T. and Xiao, S. (2012). A note on the modified two-way MANOVA tests. Statist. Prob. Lett. 82, 519-527.

2011

• Zhang, J. T. (2011b). Two-way MANOVA with unequal cell sizes and unequal cell covariance matrices. Technometrics 53(4),: 426-439.
• Zhang, J. T. (2011a). Statistical inferences for linear models with functional responses. Statistica Sinica. 21, 1431-1451.
• Sun, Y. and Zhang, J. T. (2011). A score test for variance components in a semiparametric mixed-effects model. Statistics and Its Interface 4,65-72.

2010

• Zhang, J. T. and Wu, H. (2010). Modeling HIV dynamics using unified mixed-effects models. Amer. J. Math. Manag. Sci. 30, 83-109.
• Zhang, J. T. and Sun, Y. (2010). Two-sample test for equal covariance function for functional data. Oriental J. of Mathematics,4,1-22.
• Zhang, J. T. , Liang X. and Xiao S. (2010). On the two-sample Behrens-Fisher problem for functional data. J. of Statistical Theory and Practice. 4, pp. 571-587.
• Zhang, C., Peng, H. and Zhang, J. T. (2010). Two-sample tests for functional data. Comm. Statist. Theor. Meths., 39. 559-578.
• Fan, J., Zhang, J. T., and Zhang, W. (2010). Comments on “Dynamic relations for sparsely sampled Guassian processes”. Test, 19, 37-42.

2007-2009

• Zhang, J.T. and Xu, J.F. (2009). On the k-sample Behrens-Fisher problem for high-dimensional data. Science of China: Ser. A, 52 (6): 1285-1304.
• Zhang, W., Sun, Y., Zhang, J. T., and Wang D. (2008). Local polynomial modeling for varying-coefficient informative survival models. Statistica Sinica. 19, 1319-1335.
• Zhang, J. T. and Chen, J. W. (2007). Statistical inferences for functional data. Ann. Statist. 35, 1052-1079.

2004-2005

• Zhang J. T. (2005). Approximate and asymptotic distributions of chi-squared -type mixtures with applications. J. Amer. Statist. Assoc., 100, 273-285.
• Zhang, J. T. (2004). A simple and efficient monotone smoother using smoothing splines. J. of Nonpar. Statist., 16, 779-796.
• Marron, J.S. and Zhang, J. T. (2004). SiZer for smoothing splines . Computational Statistics, 20, 481-502.
2002
• Wu, H. and Zhang, J. T. (2002a). Local polynomial mixed-effects models for longitudinal data. J. Amer. Statist. Assoc. , 97, 883-897.
• Wu, H. and Zhang, J. T. (2002b). The study of long-term HIV dynamics using semiparametric nonlinear mixed-effects models. Statistics in Medicine, 21, 3655-3675.

2000
• Fan, J. and Zhang, J. T. (2000). Two-step estimation for functional linear models with applications to longitudinal data . J.R. Statist. Soc. B. 62, 303-322.
• Zhang, J. T. and Fan, J. (2000). Minimax kernels for nonparametric curve estimation (2000) . J. of Nonpar. Statist. 12, 417-445.

1998-1999

• Locantore, N., Marron, J. S., Simpson, D.G., Tripoli, N., Zhang, J. T. and Cohen, K. L. (1999). Robust principal component analysis for functional data (with discussions and rejoinder) Test, 8, 1-73.
• Fan, J. and Zhang, J. T. (1998). Comments on “Smoothing spline models for analysis of nested and crossed samples of curves” by Brumback and Rice. J. Amer. Statist. Assoc. 93, 980-983.

1993-1995

• Zhang, J. T. and Fang, K. T. (1993). A new algorithm for estimating the parameters of nonlinear regression modellings. Acta Mathematicae Applicate Sinica. Vol. 16, No. 3, 366-377.
• Zhang, J. T. (1993). Uniform design for experiments with mixture. Chinese J. Appl. Prob. Statist. Vol. 9, No.2, 168-176.

Monographs

• Zhang, J. T. (2013). Analysis of Variance for Functional Data. Chapman and Hall, London.
• Wu, H. and Zhang, J. T. (2006). Nonparametric Regression Methods for Longitudinal Data: mixed-effects modeling   approaches. John Wiley and Sons, New York.

Book Edited

• Chen Z., Zhang, J. T. and Hu, F. (2008). Advances in Statistics. World scientific Publishing Co. Pte. Ltd.

• Zhang, J.-T., Wang, J. and Zhu T. (2024). Two-sample test for high-dimensional covariance matrices: a normal-reference approach. Under review.
• Zhu, T. and Zhang, J.-T. (2024). A fast and accurate kernel-based two-sample test with applications to high-dimensional and functional data. Under review.
• Munko, M., Ditzhaus, M., Pauly, M., Smaga, L., and Zhang, J.-T. (2024). General multiple tests for functional data. Under review.
• Zhou, B., Ong, Z. and Zhang, J.-T. (2024). A new MMD-based two-sample test for equal distribution in separable metric space. Under review. 

Undergraduate Courses Taught:
• Regression Analysis (for science). (4 times, 64 /91/ 86/155 students)
• Regression Analysis (for arts). (Thrice, 125/ 42/ 25 students)
• Statistical Methods for Social Sciences. (Once, 71 students)
• Computational Intensive Statistical Methods. (Thrice, 18/ 20/ 77 students)
• Bayesian Statistics (Twice, 11/26 students)
• Linear Models. (Twice, 30/55 students)
• Computer Aided Data Analysis (Once, 157 students)
• Categorical Data Analysis (Twice 137/127 students)

Graduate Courses Taught
• Nonparametric Regression. (Thrice, 22/20/71 students)
• Generalized Linear Models. (Once, 24 students)

• Yong Yee May , 2009-2012. On some of the Behrens-Fisher problems.
• Zhu Yeying, 2006-2008. Regression Spline via penalizing derivatives.
• Zhang Xiaoe, 2006-2008. Robust nonparametric regression using kernel smoothing
• Li Juanjuan, 2005-2007. Nonparametric adaptive design for clinical trials with continuous response.
• Huang Ying, 2003-2005. Principal component-based adaptive Neyman test for functional data
• Liang Yu, 2002-2004. Approximate the distribution of chi-square type mixtures via matching four cumulants.
• Zhang Wen, 2001-2003. Thresholding nonparametric regression.

 Ong Zhipeng, 2017-2022. Tests of equal distributions of samples in separate metric spaces
 Zhu Tianming, 2014-2018. Some new approaches for supervised classification of functional data.
 Zhang Liang, 2014-2018. Some scale-invariant tests for high-dimensional data.
 Guo Jia, 2012-2016. Testing the Equality of Several Covariance Functions for Functional Data.
 Zhou Bu , 2012-2016. Linear Hypothesis Testing for High-dimensional Data under Heteroscedasticity.
 Liu Xuefeng. 2009-2014. Solving Some Behrens-Fisher Problems Using Modified Bartlett Corrections.
 Xiao Shengning. 2008-2012. Modified MANOVA Tests Under Heteroscedasticity.
 Liang Xuehua. 2007-2012. On Some Hypothesis Testing Problems in Functional Data Analysis.

• Zhang Chongqi, Research Fellow (Nov 2003-May 2004) under my research grant R-155-000-038-112.
• Sun Yan, Research Fellow (Oct 2006-Feb 2007), under my research grant R-155-000-038-112.
• Liang Xuehua, Research Assistant (August 2011-June 2012), under my research grant R-155-000-108-112 and Research Fellow(April 2014-July 2014) under my research grant R-155-000-128-112.
• Xiao Shengning, Research Assistant (Sept 2012- Jan 2013) under my research grant R-155-000-108-112.
• Zhou Bu, Research Associate (Nov 2016-Nov 2017) under my grant R-155-000-128-112 and Research Fellow (Nov 2017-Feb 2018) under my research grant R-155-000-175-114.
• Guo Jia, Research Fellow (May 2017-Oct 2017) under my research grant R-155-000-175-114.
• Zhu Tianming, Research Associate (Nov 2017-Oct 2018) under my research grant R-155-000-187-114 and Research Fellow (Oct 2018-March 2019) under my research grant R-155-000-175-114 and Research Fellow (March 2019-August 2019) under my research grant R-155-000-187-114.