IDEAS home Printed from https://ideas.repec.org/a/bla/jorssb/v65y2003i1p143-157.html
   My bibliography  Save this article

Sequential classification on partially ordered sets

Author

Listed:
  • Curtis Tatsuoka
  • Thomas Ferguson

Abstract

Summary. A general theorem on the asymptotically optimal sequential selection of experiments is presented and applied to a Bayesian classification problem when the parameter space is a finite partially ordered set. The main results include establishing conditions under which the posterior probability of the true state converges to 1 almost surely and determining optimal rates of convergence. Properties of a class of experiment selection rules are explored.

Suggested Citation

  • Curtis Tatsuoka & Thomas Ferguson, 2003. "Sequential classification on partially ordered sets," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(1), pages 143-157, February.
    • Handle: RePEc:bla:jorssb:v:65:y:2003:i:1:p:143-157
    DOI: 10.1111/1467-9868.00377
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/1467-9868.00377
    Download Restriction: no
    File URL: https://libkey.io/10.1111/1467-9868.00377?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
    ---><---

    Citations

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


    Cited by:

    1. Ying Cheng, 2009. "When Cognitive Diagnosis Meets Computerized Adaptive Testing: CD-CAT," Psychometrika, Springer;The Psychometric Society, vol. 74(4), pages 619-632, December.
    2. S. Ferguson, T.Thomas & Tatsuoka, Curtis, 2004. "An optimal strategy for sequential classification on partially ordered sets," Statistics & Probability Letters, Elsevier, vol. 68(2), pages 161-168, June.
    3. Jingchen Liu & Zhiliang Ying & Stephanie Zhang, 2015. "A Rate Function Approach to Computerized Adaptive Testing for Cognitive Diagnosis," Psychometrika, Springer;The Psychometric Society, vol. 80(2), pages 468-490, June.
    4. Jared D. Huling & Menggang Yu, 2022. "Sufficient dimension reduction for populations with structured heterogeneity," Biometrics, The International Biometric Society, vol. 78(4), pages 1626-1638, December.
    5. Curtis Tatsuoka & Ferenc Varadi & Judith Jaeger, 2013. "Latent Partially Ordered Classification Models and Normal Mixtures," Journal of Educational and Behavioral Statistics, , vol. 38(3), pages 267-294, June.
    6. Chen, Yunxiao & Li, Xiaoou & Liu, Jingchen & Ying, Zhiliang, 2017. "Regularized latent class analysis with application in cognitive diagnosis," LSE Research Online Documents on Economics 103182, London School of Economics and Political Science, LSE Library.
    7. Xuliang Gao & Daxun Wang & Yan Cai & Dongbo Tu, 2020. "Cognitive Diagnostic Computerized Adaptive Testing for Polytomously Scored Items," Journal of Classification, Springer;The Classification Society, vol. 37(3), pages 709-729, October.
    8. Yunxiao Chen & Xiaoou Li & Jingchen Liu & Zhiliang Ying, 2017. "Regularized Latent Class Analysis with Application in Cognitive Diagnosis," Psychometrika, Springer;The Psychometric Society, vol. 82(3), pages 660-692, September.
    9. Wenyi Wang & Lihong Song & Teng Wang & Peng Gao & Jian Xiong, 2020. "A Note on the Relationship of the Shannon Entropy Procedure and the Jensen–Shannon Divergence in Cognitive Diagnostic Computerized Adaptive Testing," SAGE Open, , vol. 10(1), pages 21582440198, January.

    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:bla:jorssb:v:65:y:2003:i:1:p:143-157. 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: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/rssssea.html .

    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.