Review Articles

Discussion on the paper “A review of distributed statistical inference”

Junlong Zhao

School of Statistics, Beijing Normal University, Beijing, China

Pages 108-110 | Received 11 Nov. 2021, Accepted 20 Nov. 2021, Published online: 16 Dec. 2021,
  • Abstract
  • Full Article
  • References
  • Citations


  • Anderson, T. W. (2003). An introduction to multivariate statistical analysis (3rd ed.). John Wiley & Sons.
  • Bai, Z., & Saranadasa, H. (1996). Effect of high dimension: By an example of a two sample problem. Statistica Sinica, 6(2), 311329.
  • Banerjee, M., Durot, C., & Sen, B. (2019). Divide and conquer in nonstandard problems and the super-efficiency phenomenon. Annals of Statistics, 47(2), 720757.
  • Braverman, M., Garg, A., Ma, T., Nguyen, H. L., & Woodruff, D. P. (2016). Communication lower bounds for statistical estimation problems via a distributed data processing inequality. In Proceedings of the forty-eighth annual ACM symposium on theory of computing (pp. 1011–1020).
  • Cai, T. T., & Wei, H. (2020). Distributed Gaussian mean estimation under communication constraints: Optimal rates and communication-efficient algorithms. arXiv:2001.08877.
  • Du, B., & Zhao, J. (2021). Hypothesis testing of one sample mean vector in distributed frameworks. arXiv:2110.02588.
  • Garg, A., Ma, T., & Nguyen, H. (2014). On communication cost of distributed statistical estimation and dimensionality. Advances in Neural Information Processing Systems, 27, 27262734.
  • Lee, J. D., Liu, Q., Sun, Y., & Taylor, J. E. (2017). Communication-efficient sparse regression. Journal of Machine Learning Research, 18(1), 115144.
  • Li, M., & Zhao, J. (2021). Communication-efficient distributed linear discriminant analysis for binary classification. Statistica Sinica.
  • Shi, C., Lu, W., & Song, R. (2018). A massive data framework for M-estimators with cubic-rate. Journal of the American Statistical Association, 113(524), 16981709. [Taylor & Francis Online], [Web of Science ®],
  • Srivastava, M. S., & Du, M. (2008). A test for the mean vector with fewer observations than the dimension. Journal of Multivariate Analysis, 99(3), 386402.
  • Tian, L., & Gu, Q. (2017). Communication-efficient distributed sparse linear discriminant analysis. In Artificial intelligence and statistics (pp. 1178–1187).
  • Volgushev, S., Chao, S. K., & Cheng, G. (2019). Distributed inference for quantile regression processes. Annals of Statistics, 47(3), 16341662.
  • Wang, L., Peng, B., & Li, R. (2015). A high-dimensional nonparametric multivariate test for mean vector. Journal of the American Statistical Association, 110(512), 16581669.
  • Zhang, Y., Duchi, J. C., Jordan, M. I., & Wainwright, M. J. (2013). Information-theoretic lower bounds for distributed statistical estimation with communication constraints. In Neural information processing systems (pp. 2328–2336).

To cite this article: Junlong Zhao (2021): Discussion on the paper “A review of distributed
statistical inference”, Statistical Theory and Related Fields, DOI: 10.1080/24754269.2021.2015861

To link to this article: