IDEAS home Printed from https://ideas.repec.org/a/eee/stapro/v208y2024ics0167715224000476.html
   My bibliography  Save this article

On a discrete symmetric optimal associated kernel for estimating count data distributions

Author

Listed:
  • Senga Kiessé, Tristan
  • Durrieu, Gilles

Abstract

The nonparametric estimator using discrete kernels is one competing alternative to the frequency estimator. We investigate a discrete symmetric “optimal” kernel. Its properties are established and studied by simulations. An application to real count data is given.

Suggested Citation

  • Senga Kiessé, Tristan & Durrieu, Gilles, 2024. "On a discrete symmetric optimal associated kernel for estimating count data distributions," Statistics & Probability Letters, Elsevier, vol. 208(C).
    • Handle: RePEc:eee:stapro:v:208:y:2024:i:c:s0167715224000476
    DOI: 10.1016/j.spl.2024.110078
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167715224000476
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.spl.2024.110078?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. Bernard Bercu & Sami Capderou & Gilles Durrieu, 2019. "Nonparametric recursive estimation of the derivative of the regression function with application to sea shores water quality," Statistical Inference for Stochastic Processes, Springer, vol. 22(1), pages 17-40, April.
    2. Jeffrey S. Racine & Qi Li & Karen X. Yan, 2020. "Kernel smoothed probability mass functions for ordered datatypes," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 32(3), pages 563-586, July.
    3. Célestin C. Kokonendji & Sobom M. Somé & Youssef Esstafa & Marcelo Bourguignon, 2023. "On Underdispersed Count Kernels for Smoothing Probability Mass Functions," Stats, MDPI, vol. 6(4), pages 1-15, November.
    4. Abadir, Karim M. & Lawford, Steve, 2004. "Optimal asymmetric kernels," Economics Letters, Elsevier, vol. 83(1), pages 61-68, April.
    5. Claude Welcker & Nadir Abusamra Spencer & Olivier Turc & Italo Granato & Romain Chapuis & Delphine Madur & Katia Beauchene & Brigitte Gouesnard & Xavier Draye & Carine Palaffre & Josiane Lorgeou & Ste, 2022. "Physiological adaptive traits are a potential allele reservoir for maize genetic progress under challenging conditions," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    6. T. Senga Kiessé & Etienne Rivot & Christophe Jaeger & Joël Aubin, 2022. "Bayesian inference in based-kernel regression: comparison of count data of condition factor of fish in pond systems," Journal of Applied Statistics, Taylor & Francis Journals, vol. 49(3), pages 676-693, February.
    7. Alan Huang & Lucas Sippel & Thomas Fung, 2022. "Consistent second-order discrete kernel smoothing using dispersed Conway–Maxwell–Poisson kernels," Computational Statistics, Springer, vol. 37(2), pages 551-563, April.
    8. Chu, Chi-Yang & Henderson, Daniel J. & Parmeter, Christopher F., 2017. "On discrete Epanechnikov kernel functions," Computational Statistics & Data Analysis, Elsevier, vol. 116(C), pages 79-105.
    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. Doho, Libaud Rudy Aurelien & Somé, Sobom Matthieu & Banto, Jean Michel, 2023. "Inflation and west African sectoral stock price indices: An asymmetric kernel method analysis," Emerging Markets Review, Elsevier, vol. 54(C).
    2. Subal C. Kumbhakar & Christopher F. Parmeter & Valentin Zelenyuk, 2022. "Stochastic Frontier Analysis: Foundations and Advances I," Springer Books, in: Subhash C. Ray & Robert G. Chambers & Subal C. Kumbhakar (ed.), Handbook of Production Economics, chapter 8, pages 331-370, Springer.
    3. Hagmann, M. & Scaillet, O., 2007. "Local multiplicative bias correction for asymmetric kernel density estimators," Journal of Econometrics, Elsevier, vol. 141(1), pages 213-249, November.
    4. Kotlyarova, Yulia & Schafgans, Marcia M. A. & Zinde‐Walsh, Victoria, 2011. "Adapting kernel estimation to uncertain smoothness," LSE Research Online Documents on Economics 42015, London School of Economics and Political Science, LSE Library.
    5. Charpentier, Arthur & Flachaire, Emmanuel, 2015. "Log-Transform Kernel Density Estimation Of Income Distribution," L'Actualité Economique, Société Canadienne de Science Economique, vol. 91(1-2), pages 141-159, Mars-Juin.
    6. Bouezmarni, Taoufik & Rombouts, Jeroen V.K., 2010. "Nonparametric density estimation for positive time series," Computational Statistics & Data Analysis, Elsevier, vol. 54(2), pages 245-261, February.
    7. repec:cep:stiecm:/2011/557 is not listed on IDEAS
    8. Guohua Feng & Jiti Gao & Xiaohui Zhang, 2018. "Estimation of technical change and price elasticities: a categorical time–varying coefficient approach," Journal of Productivity Analysis, Springer, vol. 50(3), pages 117-138, December.
    9. Henderson, Daniel J. & Parmeter, Christopher F., 2012. "Canonical higher-order kernels for density derivative estimation," Statistics & Probability Letters, Elsevier, vol. 82(7), pages 1383-1387.
    10. Ben O’Neill, 2022. "Smallest covering regions and highest density regions for discrete distributions," Computational Statistics, Springer, vol. 37(3), pages 1229-1254, July.
    11. Jeffrey S. Racine & Qi Li & Karen X. Yan, 2020. "Kernel smoothed probability mass functions for ordered datatypes," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 32(3), pages 563-586, July.
    12. Jugurta Bouidghaghen & Laurence Moreau & Katia Beauchêne & Romain Chapuis & Nathalie Mangel & Llorenç Cabrera‐Bosquet & Claude Welcker & Matthieu Bogard & François Tardieu, 2023. "Robotized indoor phenotyping allows genomic prediction of adaptive traits in the field," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    13. Stefan Sperlich, 2022. "Comments on: hybrid semiparametric Bayesian networks," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(2), pages 335-339, June.
    14. Thornton, Michael A., 2014. "The aggregation of dynamic relationships caused by incomplete information," Journal of Econometrics, Elsevier, vol. 178(P2), pages 342-351.
    15. Spierdijk, Laura, 2008. "Nonparametric conditional hazard rate estimation: A local linear approach," Computational Statistics & Data Analysis, Elsevier, vol. 52(5), pages 2419-2434, January.
    16. Sobom M. Somé & Célestin C. Kokonendji & Nawel Belaid & Smail Adjabi & Rahma Abid, 2023. "Bayesian local bandwidths in a flexible semiparametric kernel estimation for multivariate count data with diagnostics," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(3), pages 843-865, September.
    17. Yulia Kotlyarova & Marcia M. A. Schafgans & Victoria Zinde-Walsh, 2021. "Rates of Expansions for Functional Estimators," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 19(1), pages 121-139, December.
    18. Amina Ika Micah, 2022. "Three essays on access to credit and financial shock in Nigeria," Economics PhD Theses 0422, Department of Economics, University of Sussex Business School.
    19. Mohammadi, Faezeh & Izadi, Muhyiddin & Lai, Chin-Diew, 2016. "On testing whether burn-in is required under the long-run average cost," Statistics & Probability Letters, Elsevier, vol. 110(C), pages 217-224.
    20. Mahdi Salehi & Andriette Bekker & Mohammad Arashi, 2023. "A Semi-parametric Density Estimation with Application in Clustering," Journal of Classification, Springer;The Classification Society, vol. 40(1), pages 52-78, April.
    21. Hongtao Ma & Yuan Meng & Hanfa Xing & Cansong Li, 2019. "Investigating Road-Constrained Spatial Distributions and Semantic Attractiveness for Area of Interest," Sustainability, MDPI, vol. 11(17), pages 1-15, August.

    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:stapro:v:208:y:2024:i:c:s0167715224000476. 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/wps/find/journaldescription.cws_home/622892/description#description .

    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.