IDEAS home Printed from https://ideas.repec.org/a/eee/stapro/v228y2026ics0167715225002020.html

Communication-efficient distributed robust variable selection for heterogeneous massive data

Author

Listed:
  • Zou, Hang
  • Jiang, Yunlu

Abstract

We propose a communication-efficient distributed robust variable selection method using discounted exponential regression for massive data. Theoretical properties of the proposed method are demonstrated. Simulation studies and the application to flue gas emission data illustrate the effectiveness of our approach.

Suggested Citation

  • Zou, Hang & Jiang, Yunlu, 2026. "Communication-efficient distributed robust variable selection for heterogeneous massive data," Statistics & Probability Letters, Elsevier, vol. 228(C).
    • Handle: RePEc:eee:stapro:v:228:y:2026:i:c:s0167715225002020
    DOI: 10.1016/j.spl.2025.110557
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167715225002020
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.spl.2025.110557?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Luo, Jiyu & Sun, Qiang & Zhou, Wen-Xin, 2022. "Distributed adaptive Huber regression," Computational Statistics & Data Analysis, Elsevier, vol. 169(C).
    2. 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).
    3. Xi Chen & Weidong Liu & Yichen Zhang, 2022. "First-Order Newton-Type Estimator for Distributed Estimation and Inference," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 117(540), pages 1858-1874, October.
    4. Jianqing Fan & Yongyi Guo & Kaizheng Wang, 2023. "Communication-Efficient Accurate Statistical Estimation," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 118(542), pages 1000-1010, April.
    5. Chen, Lanjue & Zhou, Yong, 2020. "Quantile regression in big data: A divide and conquer based strategy," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).
    6. Ye Fan & Nan Lin & Liqun Yu, 2024. "Distributed quantile regression for longitudinal big data," Computational Statistics, Springer, vol. 39(2), pages 751-779, April.
    7. Wang, Kangning & Li, Shaomin, 2021. "Robust distributed modal regression for massive data," Computational Statistics & Data Analysis, Elsevier, vol. 160(C).
    8. Michael I. Jordan & Jason D. Lee & Yun Yang, 2019. "Communication-Efficient Distributed Statistical Inference," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(526), pages 668-681, April.
    9. Fan J. & Li R., 2001. "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1348-1360, December.
    10. Xueqin Wang & Yunlu Jiang & Mian Huang & Heping Zhang, 2013. "Robust Variable Selection With Exponential Squared Loss," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(502), pages 632-643, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Shanshan Wang & Wei Cao & Xiaoxue Hu & Hanyu Zhong & Weixi Sun, 2025. "A Selective Overview of Quantile Regression for Large-Scale Data," Mathematics, MDPI, vol. 13(5), pages 1-30, March.
    2. Cheng Li & Ruihan Luo & Jian-Feng Yang, 2025. "Optimal distributed Poisson subsampling for modal regression with massive data," Statistical Papers, Springer, vol. 66(5), pages 1-32, August.
    3. Zhaohan Hou & Wei Ma & Lei Wang, 2023. "Sparse and debiased lasso estimation and inference for high-dimensional composite quantile regression with distributed data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 32(4), pages 1230-1250, December.
    4. 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).
    5. Yiping Yang & Peixin Zhao & Dongsheng Wu, 2025. "Robust variable selection with exponential squared loss for linear mixed-effects models," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 88(6), pages 1023-1049, August.
    6. Chen, Bingzhen & Zhai, Wenjuan, 2025. "Unified algorithms for distributed regularized linear regression model," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 229(C), pages 867-884.
    7. Wu, Jinran & Wang, You-Gan & Tian, Yu-Chu & Burrage, Kevin & Cao, Taoyun, 2021. "Support vector regression with asymmetric loss for optimal electric load forecasting," Energy, Elsevier, vol. 223(C).
    8. 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.
    9. Wentao Wang & Jiaxuan Liang & Rong Liu & Yunquan Song & Min Zhang, 2022. "A Robust Variable Selection Method for Sparse Online Regression via the Elastic Net Penalty," Mathematics, MDPI, vol. 10(16), pages 1-18, August.
    10. Jun Jin & Chenyan Hao & Yewen Chen, 2025. "Composite quantile regression for a distributed system with non-randomly distributed data," Statistical Papers, Springer, vol. 66(1), pages 1-30, January.
    11. 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).
    12. Yiping Yang & Peixin Zhao & Dongsheng Wu, 2025. "Robust variable selection with exponential squared loss for linear mixed-effects models," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 88(7), pages 981-1007, October.
    13. Wang, Kangning & Li, Shaomin, 2021. "Robust distributed modal regression for massive data," Computational Statistics & Data Analysis, Elsevier, vol. 160(C).
    14. Ioannis Kalogridis & Gerda Claeskens & Stefan Aelst, 2023. "Robust and efficient estimation of nonparametric generalized linear models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 32(3), pages 1055-1078, September.
    15. Qingguo Tang & R. J. Karunamuni, 2018. "Robust variable selection for finite mixture regression models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 70(3), pages 489-521, June.
    16. Hang Zou & Xiaowen Huang & Yunlu Jiang, 2025. "Robust variable selection for additive coefficient models," Computational Statistics, Springer, vol. 40(2), pages 977-997, February.
    17. 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.
    18. Liya Fu & Zhuoran Yang & Fengjing Cai & You-Gan Wang, 2021. "Efficient and doubly-robust methods for variable selection and parameter estimation in longitudinal data analysis," Computational Statistics, Springer, vol. 36(2), pages 781-804, June.
    19. Smucler, Ezequiel & Yohai, Victor J., 2017. "Robust and sparse estimators for linear regression models," Computational Statistics & Data Analysis, Elsevier, vol. 111(C), pages 116-130.
    20. Xiaochao Xia & Sijin He & Naiwen Pang, 2025. "Communication-efficient model averaging prediction for massive data with asymptotic optimality," Statistical Papers, Springer, vol. 66(2), pages 1-45, February.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:stapro:v:228:y:2026:i:c:s0167715225002020. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.