IDEAS home Printed from https://ideas.repec.org/a/spr/psycho/v73y2008i2p209-230.html
   My bibliography  Save this article

Bayesian IRT Guessing Models for Partial Guessing Behaviors

Author

Listed:
  • Jing Cao
  • S. Stokes

Abstract

No abstract is available for this item.

Suggested Citation

  • Jing Cao & S. Stokes, 2008. "Bayesian IRT Guessing Models for Partial Guessing Behaviors," Psychometrika, Springer;The Psychometric Society, vol. 73(2), pages 209-230, June.
  • Handle: RePEc:spr:psycho:v:73:y:2008:i:2:p:209-230
    DOI: 10.1007/s11336-007-9045-9
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11336-007-9045-9
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1007/s11336-007-9045-9?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. Paul L. Speckman, 2003. "Fully Bayesian spline smoothing and intrinsic autoregressive priors," Biometrika, Biometrika Trust, vol. 90(2), pages 289-302, June.
    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. Yu-Wei Chang & Rung-Ching Tsai & Nan-Jung Hsu, 2014. "A Speeded Item Response Model: Leave the Harder till Later," Psychometrika, Springer;The Psychometric Society, vol. 79(2), pages 255-274, April.
    2. Ernesto Martín & Jorge González & Francis Tuerlinckx, 2015. "On the Unidentifiability of the Fixed-Effects 3PL Model," Psychometrika, Springer;The Psychometric Society, vol. 80(2), pages 450-467, June.
    3. Yu-Wei Chang & Jyun-Ye Tu, 2022. "Bayesian estimation for an item response tree model for nonresponse modeling," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 85(8), pages 1023-1047, 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. Jing Cao & Chong Z. He & Kimberly M. Suedkamp Wells & Joshua J. Millspaugh & Mark R. Ryan, 2009. "Modeling Age and Nest-Specific Survival Using a Hierarchical Bayesian Approach," Biometrics, The International Biometric Society, vol. 65(4), pages 1052-1062, December.
    2. Yu Yue & Paul Speckman & Dongchu Sun, 2012. "Priors for Bayesian adaptive spline smoothing," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 64(3), pages 577-613, June.
    3. Tong, Xiaojun & He, Zhuoqiong Chong & Sun, Dongchu, 2018. "Estimating Chinese Treasury yield curves with Bayesian smoothing splines," Econometrics and Statistics, Elsevier, vol. 8(C), pages 94-124.
    4. Takemi Yanagimoto & Toshio Ohnishi, 2014. "Permissible boundary prior function as a virtually proper prior density," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 66(4), pages 789-809, August.
    5. Dongchu Sun & Paul Speckman, 2008. "Bayesian hierarchical linear mixed models for additive smoothing splines," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 60(3), pages 499-517, September.
    6. Robert T. Krafty & Ori Rosen & David S. Stoffer & Daniel J. Buysse & Martica H. Hall, 2017. "Conditional Spectral Analysis of Replicated Multiple Time Series With Application to Nocturnal Physiology," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(520), pages 1405-1416, October.
    7. Cheng, Chin-I. & Speckman, Paul L., 2012. "Bayesian smoothing spline analysis of variance," Computational Statistics & Data Analysis, Elsevier, vol. 56(12), pages 3945-3958.
    8. Theodore Eisenberg & Thomas Eisenberg & Martin T. Wells & Min Zhang, 2015. "Addressing the Zeros Problem: Regression Models for Outcomes with a Large Proportion of Zeros, with an Application to Trial Outcomes," Journal of Empirical Legal Studies, John Wiley & Sons, vol. 12(1), pages 161-186, March.
    9. Krivobokova, Tatyana & Serra, Paulo & Rosales, Francisco & Klockmann, Karolina, 2022. "Joint non-parametric estimation of mean and auto-covariances for Gaussian processes," Computational Statistics & Data Analysis, Elsevier, vol. 173(C).
    10. Peter F. Craigmile & Peter Guttorp, 2022. "Rejoinder to the discussion on “A combined estimate of global temperature”," Environmetrics, John Wiley & Sons, Ltd., vol. 33(3), May.

    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:spr:psycho:v:73:y:2008:i:2:p:209-230. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.