IDEAS home Printed from https://ideas.repec.org/a/taf/jnlasa/v118y2023i543p1849-1861.html
   My bibliography  Save this article

Sparse Topic Modeling: Computational Efficiency, Near-Optimal Algorithms, and Statistical Inference

Author

Listed:
  • Ruijia Wu
  • Linjun Zhang
  • T. Tony Cai

Abstract

Sparse topic modeling under the probabilistic latent semantic indexing (pLSI) model is studied. Novel and computationally fast algorithms for estimation and inference of both the word-topic matrix and the topic-document matrix are proposed and their theoretical properties are investigated. Both minimax upper and lower bounds are established and the results show that the proposed algorithms are rate-optimal, up to a logarithmic factor. Moreover, a refitting algorithm is proposed to establish asymptotic normality and construct valid confidence intervals for the individual entries of the word-topic and topic-document matrices. Simulation studies are carried out to investigate the numerical performance of the proposed algorithms. The results show that the proposed algorithms perform well numerically and are more accurate in a range of simulation settings comparing to the existing literature. In addition, the methods are illustrated through an analysis of the COVID-19 Open Research Dataset (CORD-19).

Suggested Citation

  • Ruijia Wu & Linjun Zhang & T. Tony Cai, 2023. "Sparse Topic Modeling: Computational Efficiency, Near-Optimal Algorithms, and Statistical Inference," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 118(543), pages 1849-1861, July.
    • Handle: RePEc:taf:jnlasa:v:118:y:2023:i:543:p:1849-1861
    DOI: 10.1080/01621459.2021.2018329
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/01621459.2021.2018329
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/01621459.2021.2018329?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 search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zheng Tracy Ke & Jingming Wang, 2024. "Entry-Wise Eigenvector Analysis and Improved Rates for Topic Modeling on Short Documents," Mathematics, MDPI, vol. 12(11), pages 1-41, May.
    2. Laura Battaglia & Timothy M. Christensen & Stephen Hansen & Szymon Sacher, 2024. "Inference for regression with variables generated from unstructured data," CeMMAP working papers 10/24, Institute for Fiscal Studies.

    More about this item

    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:taf:jnlasa:v:118:y:2023:i:543:p:1849-1861. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/UASA20 .

    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.