IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v211y2025ics0167947325000799.html

Distributed variable screening for generalized linear models

Author

Listed:
  • Diao, Tianbo
  • Li, Bo
  • Qu, Lianqiang
  • Sun, Liuquan

Abstract

In this article, we develop a distributed variable screening method for generalized linear models. This method is designed to handle situations where both the sample size and the number of covariates are large. Specifically, the proposed method selects relevant covariates by using a sparsity-restricted surrogate likelihood estimator. It takes into account the joint effects of the covariates rather than just the marginal effect, and this characteristic enhances the reliability of the screening results. We establish the sure screening property of the proposed method, which ensures that with a high probability, the true model is included in the selected model. Simulation studies are conducted to evaluate the finite sample performance of the proposed method, and an application to a real dataset showcases its practical utility.

Suggested Citation

  • Diao, Tianbo & Li, Bo & Qu, Lianqiang & Sun, Liuquan, 2025. "Distributed variable screening for generalized linear models," Computational Statistics & Data Analysis, Elsevier, vol. 211(C).
    • Handle: RePEc:eee:csdana:v:211:y:2025:i:c:s0167947325000799
    DOI: 10.1016/j.csda.2025.108203
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167947325000799
    Download Restriction: Full text for ScienceDirect subscribers only.
    File URL: https://libkey.io/10.1016/j.csda.2025.108203?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. Tingyou Zhou & Liping Zhu & Chen Xu & Runze Li, 2020. "Model-Free Forward Screening Via Cumulative Divergence," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(531), pages 1393-1405, July.
    2. Yuan Gao & Weidong Liu & Hansheng Wang & Xiaozhou Wang & Yibo Yan & Riquan Zhang, 2022. "Rejoinder on ‘A review of distributed statistical inference’," Statistical Theory and Related Fields, Taylor & Francis Journals, vol. 6(2), pages 111-113, May.
    3. Chen Xu & Jiahua Chen, 2014. "The Sparse MLE for Ultrahigh-Dimensional Feature Screening," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(507), pages 1257-1269, September.
    4. Yuan Gao & Weidong Liu & Hansheng Wang & Xiaozhou Wang & Yibo Yan & Riquan Zhang, 2022. "A review of distributed statistical inference," Statistical Theory and Related Fields, Taylor & Francis Journals, vol. 6(2), pages 89-99, May.
    5. Jianqing Fan & Jinchi Lv, 2008. "Sure independence screening for ultrahigh dimensional feature space," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(5), pages 849-911, November.
    6. Runze Li & Wei Zhong & Liping Zhu, 2012. "Feature Screening via Distance Correlation Learning," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(499), pages 1129-1139, September.
    7. Wang, Hansheng, 2009. "Forward Regression for Ultra-High Dimensional Variable Screening," Journal of the American Statistical Association, American Statistical Association, vol. 104(488), pages 1512-1524.
    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.
    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. Lu, Jun & Lin, Lu, 2018. "Feature screening for multi-response varying coefficient models with ultrahigh dimensional predictors," Computational Statistics & Data Analysis, Elsevier, vol. 128(C), pages 242-254.
    2. Qiu, Debin & Ahn, Jeongyoun, 2020. "Grouped variable screening for ultra-high dimensional data for linear model," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).
    3. Liming Wang & Xingxiang Li & Xiaoqing Wang & Peng Lai, 2022. "Unified mean-variance feature screening for ultrahigh-dimensional regression," Computational Statistics, Springer, vol. 37(4), pages 1887-1918, September.
    4. Randall Reese & Guifang Fu & Geran Zhao & Xiaotian Dai & Xiaotian Li & Kenneth Chiu, 2022. "Epistasis Detection via the Joint Cumulant," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 14(3), pages 514-532, December.
    5. Xiang, Pengcheng & Zhou, Ling & Tang, Lu, 2024. "Transfer learning via random forests: A one-shot federated approach," Computational Statistics & Data Analysis, Elsevier, vol. 197(C).
    6. Linh H. Nghiem & Francis K.C. Hui & Samuel Müller & A.H. Welsh, 2023. "Screening methods for linear errors‐in‐variables models in high dimensions," Biometrics, The International Biometric Society, vol. 79(2), pages 926-939, June.
    7. Loann David Denis Desboulets, 2018. "A Review on Variable Selection in Regression Analysis," Econometrics, MDPI, vol. 6(4), pages 1-27, November.
    8. Zhu, Zhengyu & Liu, Jicai & Zhang, Riquan, 2026. "A novel martingale difference correlation via data splitting with applications in feature screening," Journal of Multivariate Analysis, Elsevier, vol. 211(C).
    9. Xiaochao Xia & Hao Ming, 2022. "A Flexibly Conditional Screening Approach via a Nonparametric Quantile Partial Correlation," Mathematics, MDPI, vol. 10(24), pages 1-32, December.
    10. Zhao, Bangxin & Liu, Xin & He, Wenqing & Yi, Grace Y., 2021. "Dynamic tilted current correlation for high dimensional variable screening," Journal of Multivariate Analysis, Elsevier, vol. 182(C).
    11. Zhang, Shucong & Zhou, Yong, 2018. "Variable screening for ultrahigh dimensional heterogeneous data via conditional quantile correlations," Journal of Multivariate Analysis, Elsevier, vol. 165(C), pages 1-13.
    12. Haofeng Wang & Hongxia Jin & Xuejun Jiang & Jingzhi Li, 2022. "Model Selection for High Dimensional Nonparametric Additive Models via Ridge Estimation," Mathematics, MDPI, vol. 10(23), pages 1-22, December.
    13. Dai, Linlin & Chen, Kani & Sun, Zhihua & Liu, Zhenqiu & Li, Gang, 2018. "Broken adaptive ridge regression and its asymptotic properties," Journal of Multivariate Analysis, Elsevier, vol. 168(C), pages 334-351.
    14. Li, Xingxiang & Cheng, Guosheng & Wang, Liming & Lai, Peng & Song, Fengli, 2017. "Ultrahigh dimensional feature screening via projection," Computational Statistics & Data Analysis, Elsevier, vol. 114(C), pages 88-104.
    15. Zhang, Yuying & Deng, Shiming & Wang, Wanpeng, 2026. "Feature-based profitability evaluation for newsvendor-type products," Omega, Elsevier, vol. 141(C).
    16. Sheng, Ying & Wang, Qihua, 2020. "Model-free feature screening for ultrahigh dimensional classification," Journal of Multivariate Analysis, Elsevier, vol. 178(C).
    17. Lyu Ni & Fang Fang & Fangjiao Wan, 2017. "Adjusted Pearson Chi-Square feature screening for multi-classification with ultrahigh dimensional data," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 80(6), pages 805-828, November.
    18. Dong, Yuexiao & Yu, Zhou & Zhu, Liping, 2020. "Model-free variable selection for conditional mean in regression," Computational Statistics & Data Analysis, Elsevier, vol. 152(C).
    19. Shuaishuai Chen & Jun Lu, 2023. "Quantile-Composited Feature Screening for Ultrahigh-Dimensional Data," Mathematics, MDPI, vol. 11(10), pages 1-21, May.
    20. Jiujing Wu & Hengjian Cui, 2024. "Model-free feature screening based on Hellinger distance for ultrahigh dimensional data," Statistical Papers, Springer, vol. 65(9), pages 5903-5930, December.

    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:csdana:v:211:y:2025:i:c:s0167947325000799. 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/locate/csda .

    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.