IDEAS home Printed from https://ideas.repec.org/a/spr/compst/v40y2025i2d10.1007_s00180-024-01507-z.html
   My bibliography  Save this article

Advancements in Rényi entropy and divergence estimation for model assessment

Author

Listed:
  • Luai Al-Labadi

    (University of Toronto Mississauga)

  • Zhirui Chu

    (University of Toronto Mississauga)

  • Ying Xu

    (University of Toronto Mississauga)

Abstract

Entropy and divergence, fundamental concepts in machine learning and computer science, have gained significant traction over the past decade. Statisticians have been developing estimators for these measures, advancing computational analysis. In this paper, we present nonparametric estimators for Rényi entropy and divergence. Through a range of examples, we showcase the effectiveness of our approach, demonstrating its applicability across various contexts. Furthermore, we leverage these estimators for model assessment.

Suggested Citation

  • Luai Al-Labadi & Zhirui Chu & Ying Xu, 2025. "Advancements in Rényi entropy and divergence estimation for model assessment," Computational Statistics, Springer, vol. 40(2), pages 633-650, February.
    • Handle: RePEc:spr:compst:v:40:y:2025:i:2:d:10.1007_s00180-024-01507-z
    DOI: 10.1007/s00180-024-01507-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00180-024-01507-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s00180-024-01507-z?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.

    References listed on IDEAS

    as
    1. Ebrahimi, Nader & Pflughoeft, Kurt & Soofi, Ehsan S., 1994. "Two measures of sample entropy," Statistics & Probability Letters, Elsevier, vol. 20(3), pages 225-234, June.
    2. Dey, Dipak K. & Birmiwal, Lea R., 1994. "Robust Bayesian analysis using divergence measures," Statistics & Probability Letters, Elsevier, vol. 20(4), pages 287-294, July.
    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. Wen Chen & Jinjie Wang & Jianli Ding & Xiangyu Ge & Lijing Han & Shaofeng Qin, 2023. "Detecting Long-Term Series Eco-Environmental Quality Changes and Driving Factors Using the Remote Sensing Ecological Index with Salinity Adaptability (RSEI SI ): A Case Study in the Tarim River Basin,," Land, MDPI, vol. 12(7), pages 1-23, June.
    2. Lyasmine Harrouche & Hocine Fellag & Lynda Atil, 2025. "Bayesian prior robustness using general $$\phi $$ ϕ -divergence measure," Statistical Papers, Springer, vol. 66(1), pages 1-19, January.
    3. Menéndez, M. & Morales, D. & Pardo, L., 1997. "Maximum entropy principle and statistical inference on condensed ordered data," Statistics & Probability Letters, Elsevier, vol. 34(1), pages 85-93, May.
    4. Abhik Ghosh & Ayanendranath Basu, 2016. "Robust Bayes estimation using the density power divergence," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 68(2), pages 413-437, April.
    5. Basu, Sanjib, 2000. "Uniform stability of posteriors," Statistics & Probability Letters, Elsevier, vol. 46(1), pages 53-58, January.
    6. Jan Rosenzweig, 2023. "A Tale of Tail Covariances (and Diversified Tails)," Papers 2302.13646, arXiv.org.
    7. Luai Al-Labadi & Forough Fazeli Asl & Ce Wang, 2021. "Measuring Bayesian Robustness Using Rényi Divergence," Stats, MDPI, vol. 4(2), pages 1-18, March.
    8. Goh, Gyuhyeong & Dey, Dipak K., 2014. "Bayesian model diagnostics using functional Bregman divergence," Journal of Multivariate Analysis, Elsevier, vol. 124(C), pages 371-383.
    9. Guoxin Qiu & Kai Jia, 2018. "Extropy estimators with applications in testing uniformity," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 30(1), pages 182-196, January.
    10. Giles Hooker & Anand Vidyashankar, 2014. "Bayesian model robustness via disparities," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(3), pages 556-584, September.
    11. Hanan H. Sakr & Mohamed Said Mohamed, 2024. "Sharma–Taneja–Mittal Entropy and Its Application of Obesity in Saudi Arabia," Mathematics, MDPI, vol. 12(17), pages 1-23, August.
    12. Nader Ebrahimi, 2001. "Testing for Uniformity of the Residual Life Time Based on Dynamic Kullback-Leibler Information," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 53(2), pages 325-337, June.
    13. Haiyan Zheng & Thomas Jaki & James M.S. Wason, 2023. "Bayesian sample size determination using commensurate priors to leverage preexperimental data," Biometrics, The International Biometric Society, vol. 79(2), pages 669-683, June.
    14. Adriano Suzuki & Vicente Cancho & Francisco Louzada, 2016. "The Poisson–Inverse-Gaussian regression model with cure rate: a Bayesian approach and its case influence diagnostics," Statistical Papers, Springer, vol. 57(1), pages 133-159, March.
    15. Qingrui Wang & Zhao Yao, 2025. "Bayesian influence diagnostics for a multivariate GARCH model," Statistical Papers, Springer, vol. 66(2), pages 1-27, February.
    16. Hadi Alizadeh Noughabi & Albert Vexler, 2016. "An efficient correction to the density-based empirical likelihood ratio goodness-of-fit test for the inverse Gaussian distribution," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(16), pages 2988-3003, December.
    17. Gyuhyeong Goh & Jae Kwang Kim, 2021. "Accounting for model uncertainty in multiple imputation under complex sampling," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(3), pages 930-949, September.

    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:spr:compst:v:40:y:2025:i:2:d:10.1007_s00180-024-01507-z. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.