IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v41y2003i3-4p453-464.html
   My bibliography  Save this article

Asymptotic theory for maximum likelihood in nonparametric mixture models

Author

Listed:
  • van de Geer, Sara

Abstract

No abstract is available for this item.

Suggested Citation

  • van de Geer, Sara, 2003. "Asymptotic theory for maximum likelihood in nonparametric mixture models," Computational Statistics & Data Analysis, Elsevier, vol. 41(3-4), pages 453-464, January.
    • Handle: RePEc:eee:csdana:v:41:y:2003:i:3-4:p:453-464
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-9473(02)00188-3
    Download Restriction: Full text for ScienceDirect subscribers only.
    ---><---

    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. V. D. Geer, S., 1995. "Asymptotic Normality in Mixture Models," SFB 373 Discussion Papers 1995,12, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    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. Bohning, Dankmar & Seidel, Wilfried, 2003. "Editorial: recent developments in mixture models," Computational Statistics & Data Analysis, Elsevier, vol. 41(3-4), pages 349-357, January.
    2. Pledger, Shirley & Arnold, Richard, 2014. "Multivariate methods using mixtures: Correspondence analysis, scaling and pattern-detection," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 241-261.
    3. Dankmar Böhning & Ronny Kuhnert, 2006. "Equivalence of Truncated Count Mixture Distributions and Mixtures of Truncated Count Distributions," Biometrics, The International Biometric Society, vol. 62(4), pages 1207-1215, December.

    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. Catia Scricciolo, 2018. "Bayes and maximum likelihood for $$L^1$$ L 1 -Wasserstein deconvolution of Laplace mixtures," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 27(2), pages 333-362, June.
    2. Autin Florent & Pouet Christophe, 2011. "Test on components of mixture densities," Statistics & Risk Modeling, De Gruyter, vol. 28(4), pages 389-410, December.

    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:eee:csdana:v:41:y:2003:i:3-4:p:453-464. 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.