Publications

(lab advisee and other student co-authors are marked by *)

1. Zhang, T. and Xu, B.* (2024). Tail spectral density estimation and its uncertainty quantification: another look at tail dependent time series analysis. Journal of the American Statistical Association, forthcoming. [Link]

2. Wang, W.* and Zhang, T. (2024). A warped self-normalized two-sample test for time series with staggered observation periods. Statistica Sinica, forthcoming. [Link]

3. Zhang, T. and Shao, Y.* (2024). Time-varying correlation for noncentered nonstationary time series: simultaneous inference and visualization. Statistica Sinica, forthcoming. [Link]

4. Bai, S. and Zhang, T. (2023). Tail adversarial stability for regularly varying linear processes and their extensions. Extremes, 27(1): 33—65. [Link]

5. Zhang, T., Wang, W.* and Shao, Y.* (2023). A stratified penalization method for semiparametric variable labeling of multi-output time-varying coefficient models. Statistica Sinica, 33(2): 1025—1045. [Link]

6. Zhang, T. (2022). Asymptotics of sample tail autocorrelations for tail dependent time series: phase transition and visualization. Biometrika, 109(2): 521—534. [Link]

7. Liu, X. and Zhang, T. (2022). Estimating change-point latent factor models for high-dimensional time series. Journal of Statistical Planning and Inference, 217: 69—91. [Link]

8. Zhang, T. (2021). High-quantile regression for tail-dependent time series. Biometrika 108(1): 113—126. [Link]

9. Zhang, T. (2021). Discussion of On studying extreme values and systematic risks with nonlinear time series models and tail dependence measures. Statistical Theory and Related Fields 5(1): 35—36. [Link]

10. Zhang, T., Lavitas, L.* and Pan, Q.* (2019). Asymptotic behavior of optimal weighting in generalized self-normalization for time series. Journal of Time Series Analysis 40(5): 831—851. [Link]

11. Taqqu, M. S. and Zhang, T. (2019). A self-normalized semiparametric test to detect changes in the long memory parameter. Journal of Time Series Analysis 40(4): 411—424. [Link]

12. Lavitas, L.* and Zhang, T. (2018). A time-symmetric self-normalization approach for inference of time series. Journal of Time Series Analysis 39(5): 748—762. [Link]

13. Zhang, T. and Lavitas, L.* (2018). Unsupervised self-normalized change-point testing for time series. Journal of the American Statistical Association 113(522): 637—648. [Link]

14. Zhang, T. (2018). A thresholding-based prewhitened long-run variance estimator and its dependence-oracle property. Statistica Sinica 28(1): 319—338. [Link]

15. Zhang, T. (2016). Testing additive assumptions on means of regular monitoring data: a multivariate nonstationary time series approach. Statistica Sinica 26(4): 1611—1630. [Link]

16. Bai, S.*, Taqqu, M. S. and Zhang, T. (2016). A unified approach to self-normalized block sampling. Stochastic Processes and their Applications 126(8): 2465—2493. [Link]

17. Zhang, T. (2016). Testing for jumps in the presence of smooth changes in trends of nonstationary time series. Electronic Journal of Statistics 10(1): 706—735. [Link]

18. Kim, K. H., Zhang, T. and Wu, W. B. (2015). Parametric specification test for nonlinear autoregressive models. Econometric Theory 31(5): 1078—1101. [Link]

19. Zhang, T. (2015). Semiparametric model building for regression models with time-varying parameters. Journal of Econometrics 187(1): 189—200. [Link]

20. Zhang, T. and Wu, W. B. (2015). Time-varying nonlinear regression models: Nonparametric estimation and model selection. The Annals of Statistics 43(2): 741—768. [Link]

21. Zhang, T., Ho, H.-C., Wendler, M. and Wu, W. B. (2013). Block sampling under strong dependence. Stochastic Processes and their Applications 123(6): 2323—2339. [Link]

22. Zhang, T. (2013). Clustering high-dimensional time series based on parallelism. Journal of the American Statistical Association 108(502): 577—588. [Link]

23. Zhang, T. and Wu, W. B. (2012). Inference of time-varying regression models. The Annals of Statistics 40(3): 1376—1402. [Link]

24. Degras, D., Xu, Z., Zhang, T. and Wu, W. B. (2012). Testing for parallelism among trends in multiple time series. IEEE Transactions on Signal Processing 60(3): 1087—1097. [Link]

25. Zhang, T. and Wu, W. B. (2011). Testing parametric assumptions of trends of a nonstationary time series. Biometrika 98(3): 599—614. [Link]