IDEAS home Printed from https://ideas.repec.org/p/ehl/lserod/121641.html
   My bibliography  Save this paper

Using hierarchical information-theoretic criteria to optimize subsampling of extensive datasets

Author

Listed:
  • Duarte, Belmiro P.M.
  • Atkinson, Anthony C.
  • Oliveira, Nuno M.C.

Abstract

This paper addresses the challenge of subsampling large datasets, aiming to generate a smaller dataset that retains a significant portion of the original information. To achieve this objective, we present a subsampling algorithm that integrates hierarchical data partitioning with a specialized tool tailored to identify the most informative observations within a dataset for a specified underlying linear model, not necessarily first-order, relating responses and inputs. The hierarchical data partitioning procedure systematically and incrementally aggregates information from smaller-sized samples into new samples. Simultaneously, our selection tool employs Semidefinite Programming for numerical optimization to maximize the information content of the chosen observations. We validate the effectiveness of our algorithm through extensive testing, using both benchmark and real-world datasets. The real-world dataset is related to the physicochemical characterization of white variants of Portuguese Vinho Verde. Our results are highly promising, demonstrating the algorithm's capability to efficiently identify and select the most informative observations while keeping computational requirements at a manageable level.

Suggested Citation

  • Duarte, Belmiro P.M. & Atkinson, Anthony C. & Oliveira, Nuno M.C., 2024. "Using hierarchical information-theoretic criteria to optimize subsampling of extensive datasets," LSE Research Online Documents on Economics 121641, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:121641
    as

    Download full text from publisher

    File URL: http://eprints.lse.ac.uk/121641/
    File Function: Open access version.
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Torsten Reuter & Rainer Schwabe, 2023. "Optimal subsampling design for polynomial regression in one covariate," Statistical Papers, Springer, vol. 64(4), pages 1095-1117, August.
    2. Jun Yu & HaiYing Wang & Mingyao Ai & Huiming Zhang, 2022. "Optimal Distributed Subsampling for Maximum Quasi-Likelihood Estimators With Massive Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 117(537), pages 265-276, January.
    3. Belmiro P. M. Duarte & Weng Kee Wong, 2015. "Finding Bayesian Optimal Designs for Nonlinear Models: A Semidefinite Programming-Based Approach," International Statistical Review, International Statistical Institute, vol. 83(2), pages 239-262, August.
    4. HaiYing Wang & Min Yang & John Stufken, 2019. "Information-Based Optimal Subdata Selection for Big Data Linear Regression," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(525), pages 393-405, January.
    5. HaiYing Wang & Rong Zhu & Ping Ma, 2018. "Optimal Subsampling for Large Sample Logistic Regression," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(522), pages 829-844, April.
    6. Radoslav Harman & Lenka Filová & Peter Richtárik, 2020. "A Randomized Exchange Algorithm for Computing Optimal Approximate Designs of Experiments," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(529), pages 348-361, January.
    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. Deng, Jiayi & Huang, Danyang & Ding, Yi & Zhu, Yingqiu & Jing, Bingyi & Zhang, Bo, 2024. "Subsampling spectral clustering for stochastic block models in large-scale networks," Computational Statistics & Data Analysis, Elsevier, vol. 189(C).
    2. Yuxin Sun & Wenjun Liu & Ye Tian, 2024. "Projection-Uniform Subsampling Methods for Big Data," Mathematics, MDPI, vol. 12(19), pages 1-16, September.
    3. 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.
    4. Su, Miaomiao & Wang, Ruoyu & Wang, Qihua, 2022. "A two-stage optimal subsampling estimation for missing data problems with large-scale data," Computational Statistics & Data Analysis, Elsevier, vol. 173(C).
    5. Jun Yu & Jiaqi Liu & HaiYing Wang, 2023. "Information-based optimal subdata selection for non-linear models," Statistical Papers, Springer, vol. 64(4), pages 1069-1093, August.
    6. Jun Yu & HaiYing Wang, 2022. "Subdata selection algorithm for linear model discrimination," Statistical Papers, Springer, vol. 63(6), pages 1883-1906, December.
    7. Tianzhen Wang & Haixiang Zhang, 2022. "Optimal subsampling for multiplicative regression with massive data," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 76(4), pages 418-449, November.
    8. J. Lars Kirkby & Dang H. Nguyen & Duy Nguyen & Nhu N. Nguyen, 2022. "Inversion-free subsampling Newton’s method for large sample logistic regression," Statistical Papers, Springer, vol. 63(3), pages 943-963, June.
    9. Ziyang Wang & HaiYing Wang & Nalini Ravishanker, 2023. "Subsampling in Longitudinal Models," Methodology and Computing in Applied Probability, Springer, vol. 25(1), pages 1-29, March.
    10. Amalan Mahendran & Helen Thompson & James M. McGree, 2023. "A model robust subsampling approach for Generalised Linear Models in big data settings," Statistical Papers, Springer, vol. 64(4), pages 1137-1157, August.
    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. Hector, Emily C. & Luo, Lan & Song, Peter X.-K., 2023. "Parallel-and-stream accelerator for computationally fast supervised learning," Computational Statistics & Data Analysis, Elsevier, vol. 177(C).
    13. Sokbae Lee & Serena Ng, 2020. "An Econometric Perspective on Algorithmic Subsampling," Annual Review of Economics, Annual Reviews, vol. 12(1), pages 45-80, August.
    14. Lee, JooChul & Wang, HaiYing & Schifano, Elizabeth D., 2020. "Online updating method to correct for measurement error in big data streams," Computational Statistics & Data Analysis, Elsevier, vol. 149(C).
    15. Xiaohui Yuan & Yong Li & Xiaogang Dong & Tianqing Liu, 2022. "Optimal subsampling for composite quantile regression in big data," Statistical Papers, Springer, vol. 63(5), pages 1649-1676, October.
    16. 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.
    17. Laura Deldossi & Elena Pesce & Chiara Tommasi, 2023. "Accounting for outliers in optimal subsampling methods," Statistical Papers, Springer, vol. 64(4), pages 1119-1135, August.
    18. Àngela Sebastià Bargues & José-Luis Polo Sanz & Raúl Martín Martín, 2022. "Optimal Experimental Design for Parametric Identification of the Electrical Behaviour of Bioelectrodes and Biological Tissues," Mathematics, MDPI, vol. 10(5), pages 1-16, March.
    19. Duarte, Belmiro P.M. & Sagnol, Guillaume & Wong, Weng Kee, 2018. "An algorithm based on semidefinite programming for finding minimax optimal designs," Computational Statistics & Data Analysis, Elsevier, vol. 119(C), pages 99-117.
    20. Belmiro P. M. Duarte, 2023. "Exact Optimal Designs of Experiments for Factorial Models via Mixed-Integer Semidefinite Programming," Mathematics, MDPI, vol. 11(4), pages 1-17, February.

    More about this item

    Keywords

    hierarchical data partitioning; information-theoretic criteria; large datasets; semidefinite programming; subsampling;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

    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:ehl:lserod:121641. 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: LSERO Manager (email available below). General contact details of provider: https://edirc.repec.org/data/lsepsuk.html .

    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.