IDEAS home Printed from https://ideas.repec.org/a/bla/jorssc/v66y2017i2p295-311.html
   My bibliography  Save this article

Simultaneous inference for multilevel linear mixed models—with an application to a large-scale school meal study

Author

Listed:
  • Christian Ritz
  • Rikke Pilmann Laursen
  • Camilla Trab Damsgaard

Abstract

No abstract is available for this item.

Suggested Citation

  • Christian Ritz & Rikke Pilmann Laursen & Camilla Trab Damsgaard, 2017. "Simultaneous inference for multilevel linear mixed models—with an application to a large-scale school meal study," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(2), pages 295-311, February.
    • Handle: RePEc:bla:jorssc:v:66:y:2017:i:2:p:295-311
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/rssc.12161
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Xihong Lin & Louise Ryan & Mary Sammel & Daowen Zhang & Chantana Padungtod & Xiping Xu, 2000. "A Scaled Linear Mixed Model for Multiple Outcomes," Biometrics, The International Biometric Society, vol. 56(2), pages 593-601, June.
    2. Steffen Fieuws & Geert Verbeke, 2006. "Pairwise Fitting of Mixed Models for the Joint Modeling of Multivariate Longitudinal Profiles," Biometrics, The International Biometric Society, vol. 62(2), pages 424-431, June.
    3. Christian Bressen Pipper & Christian Ritz & Hans Bisgaard, 2012. "A versatile method for confirmatory evaluation of the effects of a covariate in multiple models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 61(2), pages 315-326, March.
    4. Lee, Lung-fei, 2010. "Pooling Estimates With Different Rates Of Convergence: A Minimum Χ2 Approach With Emphasis On A Social Interactions Model," Econometric Theory, Cambridge University Press, vol. 26(1), pages 260-299, February.
    5. P. R. Rosenbaum, 2012. "Testing one hypothesis twice in observational studies," Biometrika, Biometrika Trust, vol. 99(4), pages 763-774.
    6. Richard M. Bittman & Joseph P. Romano & Carlos Vallarino & Michael Wolf, 2009. "Optimal testing of multiple hypotheses with common effect direction," Biometrika, Biometrika Trust, vol. 96(2), pages 399-410.
    7. Simon N. Wood, 2013. "A simple test for random effects in regression models," Biometrika, Biometrika Trust, vol. 100(4), pages 1005-1010.
    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. Frank Schaarschmidt & Christian Ritz & Ludwig A. Hothorn, 2022. "The Tukey trend test: Multiplicity adjustment using multiple marginal models," Biometrics, The International Biometric Society, vol. 78(2), pages 789-797, June.
    2. Dawei Li & Cheng Li & Tomio Miwa & Takayuki Morikawa, 2019. "An Exploration of Factors Affecting Drivers’ Daily Fuel Consumption Efficiencies Considering Multi-Level Random Effects," Sustainability, MDPI, vol. 11(2), pages 1-13, January.

    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. Chuan Hong & Yang Ning & Peng Wei & Ying Cao & Yong Chen, 2017. "A semiparametric model for vQTL mapping," Biometrics, The International Biometric Society, vol. 73(2), pages 571-581, June.
    2. Ji, Sisi & Zhu, Zheyi, 2022. "Does higher education matter for health?," Cardiff Economics Working Papers E2022/4, Cardiff University, Cardiff Business School, Economics Section.
    3. Brice Ozenne & Esben Budtz-Jørgensen & Sebastian Elgaard Ebert, 2023. "Controlling the familywise error rate when performing multiple comparisons in a linear latent variable model," Computational Statistics, Springer, vol. 38(1), pages 1-23, March.
    4. Karl, Andrew T. & Yang, Yan & Lohr, Sharon L., 2014. "Computation of maximum likelihood estimates for multiresponse generalized linear mixed models with non-nested, correlated random effects," Computational Statistics & Data Analysis, Elsevier, vol. 73(C), pages 146-162.
    5. Chiara Brombin & Luigi Salmaso & Lara Fontanella & Luigi Ippoliti, 2015. "Nonparametric combination-based tests in dynamic shape analysis," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 27(4), pages 460-484, December.
    6. Siyu Heng & Dylan S. Small & Paul R. Rosenbaum, 2020. "Finding the strength in a weak instrument in a study of cognitive outcomes produced by Catholic high schools," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 935-958, June.
    7. Romano Joseph P. & Shaikh Azeem & Wolf Michael, 2011. "Consonance and the Closure Method in Multiple Testing," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-25, February.
    8. Margaux Delporte & Steffen Fieuws & Geert Molenberghs & Geert Verbeke & Simeon Situma Wanyama & Elpis Hatziagorou & Christiane De Boeck, 2022. "A joint normal‐binary (probit) model," International Statistical Review, International Statistical Institute, vol. 90(S1), pages 37-51, December.
    9. Kwonsang Lee & Dylan S. Small & Paul R. Rosenbaum, 2018. "A powerful approach to the study of moderate effect modification in observational studies," Biometrics, The International Biometric Society, vol. 74(4), pages 1161-1170, December.
    10. Paul R. Rosenbaum, 2015. "Bahadur Efficiency of Sensitivity Analyses in Observational Studies," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 205-217, March.
    11. D. B. Woodard & T. M. T. Love & S. W. Thurston & D. Ruppert & S. Sathyanarayana & S. H. Swan, 2013. "Latent factor regression models for grouped outcomes," Biometrics, The International Biometric Society, vol. 69(3), pages 785-794, September.
    12. Kruse, René-Marcel & Silbersdorff, Alexander & Säfken, Benjamin, 2022. "Model averaging for linear mixed models via augmented Lagrangian," Computational Statistics & Data Analysis, Elsevier, vol. 167(C).
    13. Giampiero Marra & Rosalba Radice & Till Bärnighausen & Simon N. Wood & Mark E. McGovern, 2017. "A Simultaneous Equation Approach to Estimating HIV Prevalence With Nonignorable Missing Responses," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 484-496, April.
    14. Nathaniel E. Helwig, 2022. "Robust Permutation Tests for Penalized Splines," Stats, MDPI, vol. 5(3), pages 1-18, September.
    15. Roel Verbelen & Katrien Antonio & Gerda Claeskens, 2018. "Unravelling the predictive power of telematics data in car insurance pricing," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 67(5), pages 1275-1304, November.
    16. Yungtai Lo, 2017. "Joint modeling of bottle use, daily milk intake from bottles, and daily energy intake in toddlers," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(13), pages 2301-2316, October.
    17. Baey, Charlotte & Cournède, Paul-Henry & Kuhn, Estelle, 2019. "Asymptotic distribution of likelihood ratio test statistics for variance components in nonlinear mixed effects models," Computational Statistics & Data Analysis, Elsevier, vol. 135(C), pages 107-122.
    18. Holger Dette & Laura Hoyden & Sonja Kuhnt & Kirsten Schorning, 2017. "Optimal designs for thermal spraying," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(1), pages 53-72, January.
    19. Frank Schaarschmidt & Christian Ritz & Ludwig A. Hothorn, 2022. "The Tukey trend test: Multiplicity adjustment using multiple marginal models," Biometrics, The International Biometric Society, vol. 78(2), pages 789-797, June.
    20. Signe M. Jensen & Christian Ritz, 2015. "Simultaneous Inference for Model Averaging of Derived Parameters," Risk Analysis, John Wiley & Sons, vol. 35(1), pages 68-76, 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:jorssc:v:66:y:2017:i:2:p:295-311. 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: 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.