IDEAS home Printed from https://ideas.repec.org/a/bes/jnlasa/v100y2005p942-959.html
   My bibliography  Save this article

A Penalized Nonparametric Maximum Likelihood Approach to Species Richness Estimation

Author

Listed:
  • Wang, Ji-Ping Z.
  • Lindsay, Bruce G.

Abstract

No abstract is available for this item.

Suggested Citation

  • Wang, Ji-Ping Z. & Lindsay, Bruce G., 2005. "A Penalized Nonparametric Maximum Likelihood Approach to Species Richness Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 942-959, September.
  • Handle: RePEc:bes:jnlasa:v:100:y:2005:p:942-959
    as

    Download full text from publisher

    File URL: http://www.ingentaconnect.com/content/asa/jasa/2005/00000100/00000471/art00024
    File Function: full text
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    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. Yang Liu & Rong Kuang & Guanfu Liu, 2024. "Penalized likelihood inference for the finite mixture of Poisson distributions from capture-recapture data," Statistical Papers, Springer, vol. 65(5), pages 2751-2773, July.
    2. Laurent Cavalier & Nicolas Hengartner, 2009. "Estimating linear functionals in Poisson mixture models," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 21(6), pages 713-728.
    3. Wang, Ji-Ping, 2007. "A linearization procedure and a VDM/ECM algorithm for penalized and constrained nonparametric maximum likelihood estimation for mixture models," Computational Statistics & Data Analysis, Elsevier, vol. 51(6), pages 2946-2957, March.
    4. Emily B. Dennis & Byron J.T. Morgan & Martin S. Ridout, 2015. "Computational aspects of N-mixture models," Biometrics, The International Biometric Society, vol. 71(1), pages 237-246, March.
    5. Seungchul Baek & Junyong Park, 2022. "A computationally efficient approach to estimating species richness and rarefaction curve," Computational Statistics, Springer, vol. 37(4), pages 1919-1941, September.
    6. Yong Wang, 2009. "The constrained Fisher scoring method for maximum likelihood computation of a nonparametric mixing distribution," Computational Statistics, Springer, vol. 24(1), pages 67-81, February.
    7. Panagiotis Besbeas & Byron J. T. Morgan, 2017. "Variance estimation for integrated population models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 101(4), pages 439-460, October.
    8. Durot, Cécile & Huet, Sylvie & Koladjo, François & Robin, Stéphane, 2013. "Least-squares estimation of a convex discrete distribution," Computational Statistics & Data Analysis, Elsevier, vol. 67(C), pages 282-298.
    9. repec:jss:jstsof:40:i09 is not listed on IDEAS
    10. Sarantis Tsiaplias & Chew Lian Chua, 2013. "A Multivariate GARCH Model Incorporating the Direct and Indirect Transmission of Shocks," Econometric Reviews, Taylor & Francis Journals, vol. 32(2), pages 244-271, February.
    11. Chee, Chew-Seng & Wang, Yong, 2016. "Nonparametric estimation of species richness using discrete k-monotone distributions," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 107-118.
    12. Balabdaoui, Fadoua & Kulagina, Yulia, 2020. "Completely monotone distributions: Mixing, approximation and estimation of number of species," Computational Statistics & Data Analysis, Elsevier, vol. 150(C).
    13. Dankmar Böhning & Panicha Kaskasamkul & Peter G. M. Heijden, 2019. "A modification of Chao’s lower bound estimator in the case of one-inflation," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 82(3), pages 361-384, April.
    14. Dankmar Böhning & Alberto Vidal-Diez & Rattana Lerdsuwansri & Chukiat Viwatwongkasem & Mark Arnold, 2013. "A Generalization of Chao's Estimator for Covariate Information," Biometrics, The International Biometric Society, vol. 69(4), pages 1033-1042, December.
    15. Sa-aat Niwitpong & Dankmar Böhning & Peter Heijden & Heinz Holling, 2013. "Capture–recapture estimation based upon the geometric distribution allowing for heterogeneity," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 76(4), pages 495-519, May.
    16. Stefano Favaro & Antonio Lijoi & Ramsés H. Mena & Igor Prünster, 2009. "Bayesian non‐parametric inference for species variety with a two‐parameter Poisson–Dirichlet process prior," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(5), pages 993-1008, November.

    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:bes:jnlasa:v:100:y:2005:p:942-959. 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: Christopher F. Baum (email available below). General contact details of provider: http://www.amstat.org/publications/jasa/index.cfm?fuseaction=main .

    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.