IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i21p3319-d1504748.html
   My bibliography  Save this article

Asymptotic Properties of a Statistical Estimator of the Jeffreys Divergence: The Case of Discrete Distributions

Author

Listed:
  • Vladimir Glinskiy

    (Department of Business Analytics, Siberian Institute of Management—Branch of the Russian Presidential Academy of National Economy and Public Administration, Novosibirsk State University of Economics and Management, 630102 Novosibirsk, Russia
    Department of Statistics, Novosibirsk State University of Economics and Management, 630099 Novosibirsk, Russia)

  • Artem Logachov

    (Department of Business Analytics, Siberian Institute of Management—Branch of the Russian Presidential Academy of National Economy and Public Administration, Novosibirsk State University of Economics and Management, 630102 Novosibirsk, Russia
    Department of Computer Science in Economics, Novosibirsk State Technical University (NSTU), 630087 Novosibirsk, Russia)

  • Olga Logachova

    (Department of Higher Mathematics, Siberian State University of Geosystems and Technologies (SSUGT), 630108 Novosibirsk, Russia)

  • Helder Rojas

    (Escuela Profesional de Ingeniería Estadística, Universidad Nacional de Ingeniería, Lima 00051, Peru
    Department of Mathematics, Imperial College London, London SW7 2AZ, UK)

  • Lyudmila Serga

    (Department of Business Analytics, Siberian Institute of Management—Branch of the Russian Presidential Academy of National Economy and Public Administration, Novosibirsk State University of Economics and Management, 630102 Novosibirsk, Russia
    Department of Statistics, Novosibirsk State University of Economics and Management, 630099 Novosibirsk, Russia)

  • Anatoly Yambartsev

    (Department of Statistics, Institute of Mathematics and Statistics, University of São Paulo (USP), São Paulo 05508-220, Brazil)

Abstract

We investigate the asymptotic properties of the plug-in estimator for the Jeffreys divergence, the symmetric variant of the Kullback–Leibler (KL) divergence. This study focuses specifically on the divergence between discrete distributions. Traditionally, estimators rely on two independent samples corresponding to two distinct conditions. However, we propose a one-sample estimator where the condition results from a random event. We establish the estimator’s asymptotic unbiasedness (law of large numbers) and asymptotic normality (central limit theorem). Although the results are expected, the proofs require additional technical work due to the randomness of the conditions.

Suggested Citation

  • Vladimir Glinskiy & Artem Logachov & Olga Logachova & Helder Rojas & Lyudmila Serga & Anatoly Yambartsev, 2024. "Asymptotic Properties of a Statistical Estimator of the Jeffreys Divergence: The Case of Discrete Distributions," Mathematics, MDPI, vol. 12(21), pages 1-16, October.
  • Handle: RePEc:gam:jmathe:v:12:y:2024:i:21:p:3319-:d:1504748
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/21/3319/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/12/21/3319/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. David M. Blei & Alp Kucukelbir & Jon D. McAuliffe, 2017. "Variational Inference: A Review for Statisticians," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 859-877, April.
    2. Vexler, Albert & Gurevich, Gregory, 2010. "Empirical likelihood ratios applied to goodness-of-fit tests based on sample entropy," Computational Statistics & Data Analysis, Elsevier, vol. 54(2), pages 531-545, February.
    3. Alexander Bulinski & Denis Dimitrov, 2021. "Statistical Estimation of the Kullback–Leibler Divergence," Mathematics, MDPI, vol. 9(5), pages 1-36, March.
    4. Helder Rojas & Artem Logachov & Anatoly Yambartsev, 2023. "Order Book Dynamics with Liquidity Fluctuations: Asymptotic Analysis of Highly Competitive Regime," Mathematics, MDPI, vol. 11(20), pages 1-24, October.
    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. Shen Liu & Hongyan Liu, 2021. "Tagging Items Automatically Based on Both Content Information and Browsing Behaviors," INFORMS Journal on Computing, INFORMS, vol. 33(3), pages 882-897, July.
    2. Loaiza-Maya, Rubén & Smith, Michael Stanley & Nott, David J. & Danaher, Peter J., 2022. "Fast and accurate variational inference for models with many latent variables," Journal of Econometrics, Elsevier, vol. 230(2), pages 339-362.
    3. Luo, Nanyu & Ji, Feng & Han, Yuting & He, Jinbo & Zhang, Xiaoya, 2024. "Fitting item response theory models using deep learning computational frameworks," OSF Preprints tjxab, Center for Open Science.
    4. Xing Qin & Shuangge Ma & Mengyun Wu, 2023. "Two‐level Bayesian interaction analysis for survival data incorporating pathway information," Biometrics, The International Biometric Society, vol. 79(3), pages 1761-1774, September.
    5. Youngseon Lee & Seongil Jo & Jaeyong Lee, 2022. "A variational inference for the Lévy adaptive regression with multiple kernels," Computational Statistics, Springer, vol. 37(5), pages 2493-2515, November.
    6. Liu, Jie & Ye, Zifeng & Chen, Kun & Zhang, Panpan, 2024. "Variational Bayesian inference for bipartite mixed-membership stochastic block model with applications to collaborative filtering," Computational Statistics & Data Analysis, Elsevier, vol. 189(C).
    7. Nathaniel Tomasetti & Catherine Forbes & Anastasios Panagiotelis, 2019. "Updating Variational Bayes: Fast Sequential Posterior Inference," Monash Econometrics and Business Statistics Working Papers 13/19, Monash University, Department of Econometrics and Business Statistics.
    8. Gael M. Martin & David T. Frazier & Christian P. Robert, 2020. "Computing Bayes: Bayesian Computation from 1763 to the 21st Century," Monash Econometrics and Business Statistics Working Papers 14/20, Monash University, Department of Econometrics and Business Statistics.
    9. Asok K. Nanda & Shovan Chowdhury, 2021. "Shannon's Entropy and Its Generalisations Towards Statistical Inference in Last Seven Decades," International Statistical Review, International Statistical Institute, vol. 89(1), pages 167-185, April.
    10. Djohan Bonnet & Tifenn Hirtzlin & Atreya Majumdar & Thomas Dalgaty & Eduardo Esmanhotto & Valentina Meli & Niccolo Castellani & Simon Martin & Jean-François Nodin & Guillaume Bourgeois & Jean-Michel P, 2023. "Bringing uncertainty quantification to the extreme-edge with memristor-based Bayesian neural networks," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    11. Ho, Paul, 2023. "Global robust Bayesian analysis in large models," Journal of Econometrics, Elsevier, vol. 235(2), pages 608-642.
    12. Samyajoy Pal & Christian Heumann, 2025. "Revisiting Dirichlet Mixture Model: unraveling deeper insights and practical applications," Statistical Papers, Springer, vol. 66(1), pages 1-38, January.
    13. Liang, Xinbin & Liu, Zhuoxuan & Wang, Jie & Jin, Xinqiao & Du, Zhimin, 2023. "Uncertainty quantification-based robust deep learning for building energy systems considering distribution shift problem," Applied Energy, Elsevier, vol. 337(C).
    14. Gael M. Martin & David T. Frazier & Ruben Loaiza-Maya & Florian Huber & Gary Koop & John Maheu & Didier Nibbering & Anastasios Panagiotelis, 2023. "Bayesian Forecasting in the 21st Century: A Modern Review," Monash Econometrics and Business Statistics Working Papers 1/23, Monash University, Department of Econometrics and Business Statistics.
    15. Seokhyun Chung & Raed Al Kontar & Zhenke Wu, 2022. "Weakly Supervised Multi-output Regression via Correlated Gaussian Processes," INFORMS Joural on Data Science, INFORMS, vol. 1(2), pages 115-137, October.
    16. Gary Koop & Dimitris Korobilis, 2023. "Bayesian Dynamic Variable Selection In High Dimensions," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 64(3), pages 1047-1074, August.
    17. Ziqi Zhang & Xinye Zhao & Mehak Bindra & Peng Qiu & Xiuwei Zhang, 2024. "scDisInFact: disentangled learning for integration and prediction of multi-batch multi-condition single-cell RNA-sequencing data," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    18. Dimitris Korobilis & Davide Pettenuzzo, 2020. "Machine Learning Econometrics: Bayesian algorithms and methods," Working Papers 2020_09, Business School - Economics, University of Glasgow.
    19. Thayer Alshaabi & David R Dewhurst & James P Bagrow & Peter S Dodds & Christopher M Danforth, 2021. "The sociospatial factors of death: Analyzing effects of geospatially-distributed variables in a Bayesian mortality model for Hong Kong," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-20, March.
    20. Jan Prüser & Florian Huber, 2024. "Nonlinearities in macroeconomic tail risk through the lens of big data quantile regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(2), pages 269-291, March.

    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:gam:jmathe:v:12:y:2024:i:21:p:3319-:d:1504748. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.