IDEAS home Printed from https://ideas.repec.org/a/spr/psycho/v81y2016i4d10.1007_s11336-016-9520-2.html
   My bibliography  Save this article

Book Review

Author

Listed:
  • Brenden Bishop

    (The Ohio State University)

  • Minjeong Jeon

    (University of California, Los Angeles)

Abstract

No abstract is available for this item.

Suggested Citation

  • Brenden Bishop & Minjeong Jeon, 2016. "Book Review," Psychometrika, Springer;The Psychometric Society, vol. 81(4), pages 1164-1167, December.
    • Handle: RePEc:spr:psycho:v:81:y:2016:i:4:d:10.1007_s11336-016-9520-2
    DOI: 10.1007/s11336-016-9520-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11336-016-9520-2
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11336-016-9520-2?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. Geert Molenberghs & Caroline Beunckens & Cristina Sotto & Michael G. Kenward, 2008. "Every missingness not at random model has a missingness at random counterpart with equal fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(2), pages 371-388, April.
    2. Peter W. Hill & Harvey Goldstein, 1998. "Multilevel Modeling of Educational Data With Cross-Classification and Missing Identification for Units," Journal of Educational and Behavioral Statistics, , vol. 23(2), pages 117-128, June.
    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. Shu Xu & Shelley A. Blozis, 2011. "Sensitivity Analysis of Mixed Models for Incomplete Longitudinal Data," Journal of Educational and Behavioral Statistics, , vol. 36(2), pages 237-256, April.
    2. Kott Phillip S. & Liao Dan, 2018. "Calibration Weighting for Nonresponse with Proxy Frame Variables (So that Unit Nonresponse Can Be Not Missing at Random)," Journal of Official Statistics, Sciendo, vol. 34(1), pages 107-120, March.
    3. Marco Doretti & Sara Geneletti & Elena Stanghellini, 2018. "Missing Data: A Unified Taxonomy Guided by Conditional Independence," International Statistical Review, International Statistical Institute, vol. 86(2), pages 189-204, August.
    4. Hairu Wang & Zhiping Lu & Yukun Liu, 2023. "Score test for missing at random or not under logistic missingness models," Biometrics, The International Biometric Society, vol. 79(2), pages 1268-1279, June.
    5. Andrew T. Karl & Yan Yang & Sharon L. Lohr, 2013. "A Correlated Random Effects Model for Nonignorable Missing Data in Value-Added Assessment of Teacher Effects," Journal of Educational and Behavioral Statistics, , vol. 38(6), pages 577-603, December.
    6. Rianne Margaretha Schouten & Gerko Vink, 2021. "The Dance of the Mechanisms: How Observed Information Influences the Validity of Missingness Assumptions," Sociological Methods & Research, , vol. 50(3), pages 1243-1258, August.
    7. Daniel, Rhian M. & Kenward, Michael G., 2012. "A method for increasing the robustness of multiple imputation," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1624-1643.
    8. Janicki, Ryan & Malec, Donald, 2013. "A Bayesian model averaging approach to analyzing categorical data with nonignorable nonresponse," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 600-614.
    9. Susan M. Paddock & Terrance D. Savitsky, 2013. "Bayesian hierarchical semiparametric modelling of longitudinal post-treatment outcomes from open enrolment therapy groups," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 176(3), pages 795-808, June.
    10. Yuan, Ke-Hai, 2009. "Normal distribution based pseudo ML for missing data: With applications to mean and covariance structure analysis," Journal of Multivariate Analysis, Elsevier, vol. 100(9), pages 1900-1918, October.
    11. Margarita Moreno-Betancur & Grégoire Rey & Aurélien Latouche, 2015. "Direct likelihood inference and sensitivity analysis for competing risks regression with missing causes of failure," Biometrics, The International Biometric Society, vol. 71(2), pages 498-507, June.
    12. Yuriko Takeda & Toshihiro Misumi & Kouji Yamamoto, 2022. "Joint Models for Incomplete Longitudinal Data and Time-to-Event Data," Mathematics, MDPI, vol. 10(19), pages 1-7, October.
    13. Roula Tsonaka & Dimitris Rizopoulos & Geert Verbeke & Emmanuel Lesaffre, 2010. "Nonignorable Models for Intermittently Missing Categorical Longitudinal Responses," Biometrics, The International Biometric Society, vol. 66(3), pages 834-844, September.
    14. Trias Wahyuni Rakhmawati & Geert Molenberghs & Geert Verbeke & Christel Faes, 2016. "Local influence diagnostics for incomplete overdispersed longitudinal counts," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(9), pages 1722-1737, July.
    15. Maria Marino & Marco Alfó, 2015. "Latent drop-out based transitions in linear quantile hidden Markov models for longitudinal responses with attrition," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 9(4), pages 483-502, December.
    16. Bunouf, Pierre & Molenberghs, Geert & Grouin, Jean-Marie & Thijs, Herbert, 2015. "A SAS Program Combining R Functionalities to Implement Pattern-Mixture Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 68(i08).
    17. Christos Thomadakis & Loukia Meligkotsidou & Nikos Pantazis & Giota Touloumi, 2019. "Longitudinal and time‐to‐drop‐out joint models can lead to seriously biased estimates when the drop‐out mechanism is at random," Biometrics, The International Biometric Society, vol. 75(1), pages 58-68, March.
    18. Richard M. Golden & Steven S. Henley & Halbert White & T. Michael Kashner, 2019. "Consequences of Model Misspecification for Maximum Likelihood Estimation with Missing Data," Econometrics, MDPI, vol. 7(3), pages 1-27, September.
    19. Yordan P Raykov & Alexis Boukouvalas & Fahd Baig & Max A Little, 2016. "What to Do When K-Means Clustering Fails: A Simple yet Principled Alternative Algorithm," PLOS ONE, Public Library of Science, vol. 11(9), pages 1-28, September.
    20. Hines, R.J. O'Hara & Hines, W.G.S., 2010. "Indices for covariance mis-specification in longitudinal data analysis with no missing responses and with MAR drop-outs," Computational Statistics & Data Analysis, Elsevier, vol. 54(4), pages 806-815, April.

    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:spr:psycho:v:81:y:2016:i:4:d:10.1007_s11336-016-9520-2. 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.