Research Papers

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

 

  • Zhu, T., Zhang, J.-T., and Cheng, M.-Y. (2022) . One-way MANOVA for functional data via Lawley-Hotelling trace test. Journal of Multivariate Analysis.  https://www.sciencedirect.com/science/article/pii/S0047259X22000884?dgcid=coauthor.
  • 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.[ZhangGuoZhouCheng2020JASA]
• Smaga, L. and Zhang, J.-T. (2020). Linear hypothesis testing for weighted functional data with applications. Scandinavian Journal of Statistics, 47(2), 493-515.[SmagaZhang2020SJS]
• 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.[ZhangZhuZhang2020EconStat]
• 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.[ZhuZhang2020classifierFDA]

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.[GuoZhouZhang2019JASA]
• Guo, J. Zhou, B., J. W. Chen 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.[GuoZhouChenZhang2018Test]
• Smaga, L. and Zhang, J.-T. (2019). Linear hypothesis testing with functional data. Technometrics. 61(1):99-110.[SmagaZhang2018Technometrics]
• 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.[ZhangChengWuZhou2019CSDA]
• 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.[ZhouGuoChenZhang2019SII]

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.[ GuoZhouZhang2018CSDA]
• 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.[ZhangGuoZhou2017JMA]
• 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.[ZhouGuoZhang2017JSPI]
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.[ChengToshioZhang2016JASA]
• 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).[ZhangZhouGuoLiu2016JAS]
• 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.[LiuGuoZhouZhang2016SRL]
2013-2015
• Xiao, S. and Zhang, J.-T. (2016). Modified tests for heteroscedastic two-way MANOVA. J. of Advanced Statistics. p. 1-16, 1(1).[XiaoZhang2016JAS]
• 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[ZhangLiang2013SJS]
• 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.[ZhangLiu2013Metrika]

2012
• Zhang, J. T. (2012b). An approximate degrees of freedom test for heteroscedastic two-way ANOVA. J. of Statist. Plan. Infer., 142, 336-346.[Zhang2012JSPI]
• Zhang, J. T. (2012a). An approximate Hotelling T-square test for heteroscedastic one-way ANOVA. Open J. Statist. 2, 1-11.[Zhang2012OJS]
• 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.[ZhangLiu2012SII]
• Zhang, J. T. and Xiao, S. (2012). A note on the modified two-way MANOVA tests. Statist. Prob. Lett. 82, 519-527.
2011.[ZhangXiao2012SPL]
• Zhang, J. T. (2011b). Two-way MANOVA with unequal cell sizes and unequal cell covariance matrices. Technometrics 53(4),: 426-439.[Zhang2011Technometrics]
• Zhang, J. T. (2011a). Statistical inferences for linear models with functional responses. Statistica Sinica. 21, 1431-1451.[Zhang2011Sinica]
• 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.[SunZhang2011SII]
2010
• Zhang, J. T. and Wu, H. (2010). Modeling HIV dynamics using unified mixed-effects models. Amer. J. Math. Manag. Sci. 30, 83-109.[ZhangWu2010AMMS]
• Zhang, J. T. and Sun, Y. (2010). Two-sample test for equal covariance function for functional data. Oriental J. of Mathematics,4,1-22.[ZhangSun2010OJM]
• 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.[ZhangLiangXiao2010JSTP]
• Zhang, C., Peng, H. and Zhang, J. T. (2010). Two sample tests for functional data. Comm. Statist. Theor. Meths., 39. 559-578.[ZhangPengZhang2010CSTM]
• Fan, J., Zhang, J. T., and Zhang, W. (2010). Comments on “Dynamic relations for sparsely sampled Guassian processes”. Test, 19, 37-42.[FanZhangZhang2010Test]
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.[ZhangXu2009SCS]
• 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.[ZhangSunZhangWang2009Sinica]
• Zhang, J. T. and Chen, J. W. (2007). Statistical inferences for functional data. Ann. Statist. 35, 1052-1079.
2004-2005.[ZhangChen2007AOS]
• Zhang J. T. (2005). Approximate and asymptotic distributions of chi-squared -type mixtures with applications. J. Amer. Statist. Assoc., 100, 273-285.[Zhang05JASA]
• Zhang, J. T. (2004). A simple and efficient monotone smoother using smoothing splines. J. of Nonpar. Statist., 16, 779-796.[Zhang2004JNS][monofit][datasets: fuel food growth]
• Marron, J.S. and Zhang, J. T. (2004). SiZer for smoothing splines . Computational Statistics, 20, 481-502.[MarronZhang2005CS]
2002
• Wu, H. and Zhang, J. T. (2002a). Local polynomial mixed-effects models for longitudinal data. J. Amer. Statist. Assoc. , 97, 883-897.[WuZhang2002JASA]
• 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.[WuZhang2002SIM]

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.[FZJRSSB]
• Zhang, J. T. and Fan, J. (2000). Minimax kernels for nonparametric curve estimation (2000) . J. of Nonpar. Statist. 12, 417-445.[ZhangFan2000JNS]

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.[MarronSimpsonZhangCohen1999Test]
• 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.