IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v177y2023ics0167947322001621.html
   My bibliography  Save this article

Mixture-based estimation of entropy

Author

Listed:
  • Robin, Stéphane
  • Scrucca, Luca

Abstract

The entropy is a measure of uncertainty that plays a central role in information theory. When the distribution of the data is unknown, an estimate of the entropy needs to be obtained from the data sample itself. A semi-parametric estimate is proposed based on a mixture model approximation of the distribution of interest. A Gaussian mixture model is used to illustrate the accuracy and versatility of the proposal, although the estimate can rely on any type of mixture. Performance of the proposed approach is assessed through a series of simulation studies. Two real-life data examples are also provided to illustrate its use.

Suggested Citation

  • Robin, Stéphane & Scrucca, Luca, 2023. "Mixture-based estimation of entropy," Computational Statistics & Data Analysis, Elsevier, vol. 177(C).
    • Handle: RePEc:eee:csdana:v:177:y:2023:i:c:s0167947322001621
    DOI: 10.1016/j.csda.2022.107582
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167947322001621
    Download Restriction: Full text for ScienceDirect subscribers only.
    File URL: https://libkey.io/10.1016/j.csda.2022.107582?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. Ravi Varadhan & Christophe Roland, 2008. "Simple and Globally Convergent Methods for Accelerating the Convergence of Any EM Algorithm," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 35(2), pages 335-353, June.
    2. Leisch, Friedrich, 2004. "FlexMix: A General Framework for Finite Mixture Models and Latent Class Regression in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 11(i08).
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Branislav Panić & Marko Nagode & Jernej Klemenc & Simon Oman, 2022. "On Methods for Merging Mixture Model Components Suitable for Unsupervised Image Segmentation Tasks," Mathematics, MDPI, vol. 10(22), pages 1-22, November.

    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. Greg Lewis & Bora Ozaltun & Georgios Zervas, 2021. "Maximum Likelihood Estimation of Differentiated Products Demand Systems," Papers 2111.12397, arXiv.org.
    2. Christian Kleiber & Achim Zeileis, 2016. "Visualizing Count Data Regressions Using Rootograms," The American Statistician, Taylor & Francis Journals, vol. 70(3), pages 296-303, July.
    3. Lebret, Rémi & Iovleff, Serge & Langrognet, Florent & Biernacki, Christophe & Celeux, Gilles & Govaert, Gérard, 2015. "Rmixmod: The R Package of the Model-Based Unsupervised, Supervised, and Semi-Supervised Classification Mixmod Library," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 67(i06).
    4. Jurgen A. Doornik, 2018. "Accelerated Estimation of Switching Algorithms: The Cointegrated VAR Model and Other Applications," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 45(2), pages 283-300, June.
    5. Grün, Bettina & Kosmidis, Ioannis & Zeileis, Achim, 2012. "Extended Beta Regression in R: Shaken, Stirred, Mixed, and Partitioned," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(i11).
    6. Peter Willemé, 2017. "Working Paper 14-17 - Modelling unobserved heterogeneity in distribution - Finite mixtures of the Johnson family of distributions," Working Papers 1714, Federal Planning Bureau, Belgium.
    7. Marc A. Scott & Kaushik Mohan & Jacques‐Antoine Gauthier, 2020. "Model‐based clustering and analysis of life history data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 1231-1251, June.
    8. Yue, Chen & Chen, Shaojie & Sair, Haris I. & Airan, Raag & Caffo, Brian S., 2015. "Estimating a graphical intra-class correlation coefficient (GICC) using multivariate probit-linear mixed models," Computational Statistics & Data Analysis, Elsevier, vol. 89(C), pages 126-133.
    9. Grun, Bettina & Leisch, Friedrich, 2007. "Fitting finite mixtures of generalized linear regressions in R," Computational Statistics & Data Analysis, Elsevier, vol. 51(11), pages 5247-5252, July.
    10. Raoofpanah, Iman & Zamudio, César & Groening, Christopher, 2023. "Review reader segmentation based on the heterogeneous impacts of review and reviewer attributes on review helpfulness: A study involving ZIP code data," Journal of Retailing and Consumer Services, Elsevier, vol. 72(C).
    11. Salvatore Ingrassia & Simona Minotti & Giorgio Vittadini, 2012. "Local Statistical Modeling via a Cluster-Weighted Approach with Elliptical Distributions," Journal of Classification, Springer;The Classification Society, vol. 29(3), pages 363-401, October.
    12. Ahonen, Ilmari & Nevalainen, Jaakko & Larocque, Denis, 2019. "Prediction with a flexible finite mixture-of-regressions," Computational Statistics & Data Analysis, Elsevier, vol. 132(C), pages 212-224.
    13. Ciarleglio, Adam & Todd Ogden, R., 2016. "Wavelet-based scalar-on-function finite mixture regression models," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 86-96.
    14. FUKASAWA Takeshi & OHASHI Hiroshi, 2023. "Long-run Effect of a Horizontal Merger and Its Remedial Standards," Discussion papers 23001, Research Institute of Economy, Trade and Industry (RIETI).
    15. Prates, Marcos Oliveira & Lachos, Victor Hugo & Barbosa Cabral, Celso Rômulo, 2013. "mixsmsn: Fitting Finite Mixture of Scale Mixture of Skew-Normal Distributions," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 54(i12).
    16. Salvatore Ingrassia & Antonio Punzo, 2020. "Cluster Validation for Mixtures of Regressions via the Total Sum of Squares Decomposition," Journal of Classification, Springer;The Classification Society, vol. 37(2), pages 526-547, July.
    17. Chun-Yu Chang & Po-Chen Lin & Yung-Jiun Chien & Chien-Sheng Chen & Meng-Yu Wu, 2020. "Analysis of Chest-Compression Depth and Full Recoil in Two Infant Chest-Compression Techniques Performed by a Single Rescuer: Systematic Review and Meta-Analysis," IJERPH, MDPI, vol. 17(11), pages 1-17, June.
    18. McLachlan, Geoff & Lee, Sharon X, 2013. "EMMIXuskew: An R Package for Fitting Mixtures of Multivariate Skew t Distributions via the EM Algorithm," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 55(i12).
    19. Pál, László & Sándor, Zsolt, 2023. "Comparing procedures for estimating random coefficient logit demand models with a special focus on obtaining global optima," International Journal of Industrial Organization, Elsevier, vol. 88(C).
    20. Maureen Lankhuizen & Thomas De Graaff & Henri De Groot, 2012. "Product Heterogeneity, Intangible Barriers & Distance Decay: The effect of multiple dimensions of distance on trade across different product categories," ERSA conference papers ersa12p151, European Regional Science Association.

    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:177:y:2023:i:c:s0167947322001621. 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.