Communication-Efficient Distributed Statistical Inference

Citations

Cited by:

1. Wei Wang & Shou‐En Lu & Jerry Q. Cheng & Minge Xie & John B. Kostis, 2022. "Multivariate survival analysis in big data: A divide‐and‐combine approach," Biometrics, The International Biometric Society, vol. 78(3), pages 852-866, September.
2. Wang, Kangning & Li, Shaomin, 2021. "Robust distributed modal regression for massive data," Computational Statistics & Data Analysis, Elsevier, vol. 160(C).
3. Luo, Jiyu & Sun, Qiang & Zhou, Wen-Xin, 2022. "Distributed adaptive Huber regression," Computational Statistics & Data Analysis, Elsevier, vol. 169(C).
4. Yang, Yaohong & Wang, Lei & Liu, Jiamin & Li, Rui & Lian, Heng, 2023. "Communication-efficient estimation of quantile matrix regression for massive datasets," Computational Statistics & Data Analysis, Elsevier, vol. 187(C).
5. Xingcai Zhou & Yu Xiang, 2022. "ADMM-Based Differential Privacy Learning for Penalized Quantile Regression on Distributed Functional Data," Mathematics, MDPI, vol. 10(16), pages 1-28, August.
6. Bao, Yajie & Ren, Haojie, 2023. "Semi-profiled distributed estimation for high-dimensional partially linear model," Computational Statistics & Data Analysis, Elsevier, vol. 188(C).
7. Jiaming Luan & Hongwei Wang & Kangning Wang & Benle Zhang, 2022. "Robust distributed estimation and variable selection for massive datasets via rank regression," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 74(3), pages 435-450, June.
8. Xingcai Zhou & Zhaoyang Jing & Chao Huang, 2024. "Distributed Bootstrap Simultaneous Inference for High-Dimensional Quantile Regression," Mathematics, MDPI, vol. 12(5), pages 1-54, February.
9. Wang, Xiaoqian & Kang, Yanfei & Hyndman, Rob J. & Li, Feng, 2023. "Distributed ARIMA models for ultra-long time series," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1163-1184.
   • Xiaoqian Wang & Yanfei Kang & Rob J Hyndman & Feng Li, 2020. "Distributed ARIMA Models for Ultra-long Time Series," Monash Econometrics and Business Statistics Working Papers 29/20, Monash University, Department of Econometrics and Business Statistics.
10. Guangbao Guo & Guoqi Qian & Lu Lin & Wei Shao, 2021. "Parallel inference for big data with the group Bayesian method," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 84(2), pages 225-243, February.
11. Zhang, Haixiang & Wang, HaiYing, 2021. "Distributed subdata selection for big data via sampling-based approach," Computational Statistics & Data Analysis, Elsevier, vol. 153(C).
12. Yaohong Yang & Lei Wang, 2023. "Communication-efficient sparse composite quantile regression for distributed data," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 86(3), pages 261-283, April.
13. Wang, Kangning & Li, Shaomin & Zhang, Benle, 2021. "Robust communication-efficient distributed composite quantile regression and variable selection for massive data," Computational Statistics & Data Analysis, Elsevier, vol. 161(C).
14. Boris Beranger & Huan Lin & Scott Sisson, 2023. "New models for symbolic data analysis," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 17(3), pages 659-699, September.
15. Feifei Wang & Danyang Huang & Tianchen Gao & Shuyuan Wu & Hansheng Wang, 2022. "Sequential one‐step estimator by sub‐sampling for customer churn analysis with massive data sets," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1753-1786, November.
16. Zhan Liu & Xiaoluo Zhao & Yingli Pan, 2023. "Communication-efficient distributed estimation for high-dimensional large-scale linear regression," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 86(4), pages 455-485, May.
17. Chen, Canyi & Xu, Wangli & Zhu, Liping, 2022. "Distributed estimation in heterogeneous reduced rank regression: With application to order determination in sufficient dimension reduction," Journal of Multivariate Analysis, Elsevier, vol. 190(C).
18. Shaomin Li & Kangning Wang & Yong Xu, 2023. "Robust estimation for nonrandomly distributed data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 75(3), pages 493-509, June.
19. Ren, Yimeng & Li, Zhe & Zhu, Xuening & Gao, Yuan & Wang, Hansheng, 2024. "Distributed estimation and inference for spatial autoregression model with large scale networks," Journal of Econometrics, Elsevier, vol. 238(2).
20. Zhou, Ping & Yu, Zhen & Ma, Jingyi & Tian, Maozai & Fan, Ye, 2021. "Communication-efficient distributed estimator for generalized linear models with a diverging number of covariates," Computational Statistics & Data Analysis, Elsevier, vol. 157(C).
21. Shi, Jianwei & Qin, Guoyou & Zhu, Huichen & Zhu, Zhongyi, 2021. "Communication-efficient distributed M-estimation with missing data," Computational Statistics & Data Analysis, Elsevier, vol. 161(C).
22. Changgee Chang & Zhiqi Bu & Qi Long, 2023. "CEDAR: communication efficient distributed analysis for regressions," Biometrics, The International Biometric Society, vol. 79(3), pages 2357-2369, September.
23. Bikram Karmakar & Peng Liu & Gourab Mukherjee & Hai Che & Shantanu Dutta, 2022. "Improved retention analysis in freemium role‐playing games by jointly modelling players’ motivation, progression and churn," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(1), pages 102-133, January.
24. Xingcai Zhou & Hao Shen, 2022. "Communication-Efficient Distributed Learning for High-Dimensional Support Vector Machines," Mathematics, MDPI, vol. 10(7), pages 1-21, March.
25. Benny Ren & Ian Barnett, 2022. "Autoregressive mixture models for clustering time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(6), pages 918-937, November.
26. Lu Lin & Feng Li, 2023. "Global debiased DC estimations for biased estimators via pro forma regression," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 32(2), pages 726-758, June.
27. Lulu Zuo & Haixiang Zhang & HaiYing Wang & Liuquan Sun, 2021. "Optimal subsample selection for massive logistic regression with distributed data," Computational Statistics, Springer, vol. 36(4), pages 2535-2562, December.