IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v124y2018icp117-131.html
   My bibliography  Save this article

Bootstrapping longitudinal data with multiple levels of variation

Author

Listed:
  • O’Shaughnessy, P.Y.
  • Welsh, A.H.

Abstract

A set of estimators for model parameters in the framework of linear mixed models is considered for longitudinal data with multiple levels of random variation. Various bootstrap methods are assessed for making inference about the parameters including the variance components for which, typically, bootstrap confidence intervals show undercoverage. A new weighted estimating equation bootstrap, which uses different weight schemes for different parameter estimators, is proposed. It shows improved variance estimation for the variance component estimators and produces confidence intervals with better coverage for the variance components in cases with normal and non-normal errors.

Suggested Citation

  • O’Shaughnessy, P.Y. & Welsh, A.H., 2018. "Bootstrapping longitudinal data with multiple levels of variation," Computational Statistics & Data Analysis, Elsevier, vol. 124(C), pages 117-131.
    • Handle: RePEc:eee:csdana:v:124:y:2018:i:c:p:117-131
    DOI: 10.1016/j.csda.2018.02.004
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167947318300434
    Download Restriction: Full text for ScienceDirect subscribers only.
    File URL: https://libkey.io/10.1016/j.csda.2018.02.004?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. Samanta, Mayukh & Welsh, A.H., 2013. "Bootstrapping for highly unbalanced clustered data," Computational Statistics & Data Analysis, Elsevier, vol. 59(C), pages 70-81.
    2. Brajendra C. Sutradhar & Kalyan Das, 2000. "On the Accuracy of Efficiency of Estimating Equation Approach," Biometrics, The International Biometric Society, vol. 56(2), pages 622-625, June.
    3. Matías Salibián-Barrera & Stefan Aelst & Gert Willems, 2008. "Fast and robust bootstrap," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 17(1), pages 41-71, February.
    4. You-Gan Wang & Vincent J. Carey, 2004. "Unbiased Estimating Equations From Working Correlation Models for Irregularly Timed Repeated Measures," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 845-853, January.
    5. Field, C. A. & Pang, Zhen & Welsh, A. H., 2010. "Bootstrapping Robust Estimates for Clustered Data," Journal of the American Statistical Association, American Statistical Association, vol. 105(492), pages 1606-1616.
    6. C. A. Field & A. H. Welsh, 2007. "Bootstrapping clustered data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(3), pages 369-390, June.
    7. Salibian-Barrera, Matias & Van Aelst, Stefan, 2008. "Robust model selection using fast and robust bootstrap," Computational Statistics & Data Analysis, Elsevier, vol. 52(12), pages 5121-5135, August.
    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. Flores-Agreda, Daniel & Cantoni, Eva, 2019. "Bootstrap estimation of uncertainty in prediction for generalized linear mixed models," Computational Statistics & Data Analysis, Elsevier, vol. 130(C), pages 1-17.
    2. Reluga, Katarzyna & Lombardía, María-José & Sperlich, Stefan, 2024. "Bootstrap-based statistical inference for linear mixed effects under misspecifications," Computational Statistics & Data Analysis, Elsevier, vol. 199(C).

    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. Samanta, Mayukh & Welsh, A.H., 2013. "Bootstrapping for highly unbalanced clustered data," Computational Statistics & Data Analysis, Elsevier, vol. 59(C), pages 70-81.
    2. Valéry Dongmo Jiongo & Pierre Nguimkeu, 2018. "Bootstrapping Mean Squared Errors of Robust Small-Area Estimators: Application to the Method-of-Payments Data," Staff Working Papers 18-28, Bank of Canada.
    3. Reluga, Katarzyna & Lombardía, María-José & Sperlich, Stefan, 2024. "Bootstrap-based statistical inference for linear mixed effects under misspecifications," Computational Statistics & Data Analysis, Elsevier, vol. 199(C).
    4. Salibian-Barrera, Matias & Van Aelst, Stefan & Yohai, Víctor J., 2016. "Robust tests for linear regression models based on τ-estimates," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 436-455.
    5. La Vecchia, Davide & Camponovo, Lorenzo & Ferrari, Davide, 2015. "Robust heart rate variability analysis by generalized entropy minimization," Computational Statistics & Data Analysis, Elsevier, vol. 82(C), pages 137-151.
    6. Jiao, Xiyu & Pretis, Felix & Schwarz, Moritz, 2024. "Testing for coefficient distortion due to outliers with an application to the economic impacts of climate change," Journal of Econometrics, Elsevier, vol. 239(1).
    7. La Vecchia, Davide & Moor, Alban & Scaillet, Olivier, 2023. "A higher-order correct fast moving-average bootstrap for dependent data," Journal of Econometrics, Elsevier, vol. 235(1), pages 65-81.
    8. Janice L. Scealy & David Heslop & Jia Liu & Andrew T. A. Wood, 2022. "Directions Old and New: Palaeomagnetism and Fisher (1953) Meet Modern Statistics," International Statistical Review, International Statistical Institute, vol. 90(2), pages 237-258, August.
    9. Angela Calvo & Christian Preti & Maria Caria & Roberto Deboli, 2019. "Vibration and Noise Transmitted by Agricultural Backpack Powered Machines Critically Examined Using the Current Standards," IJERPH, MDPI, vol. 16(12), pages 1-20, June.
    10. Cheng, Guang & Yu, Zhuqing & Huang, Jianhua Z., 2013. "The cluster bootstrap consistency in generalized estimating equations," Journal of Multivariate Analysis, Elsevier, vol. 115(C), pages 33-47.
    11. Khan, Jafar A. & Van Aelst, Stefan & Zamar, Ruben H., 2010. "Fast robust estimation of prediction error based on resampling," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3121-3130, December.
    12. Riani, Marco & Atkinson, Anthony C., 2010. "Robust model selection with flexible trimming," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3300-3312, December.
    13. Alfons, Andreas & Croux, Christophe & Gelper, Sarah, 2016. "Robust groupwise least angle regression," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 421-435.
    14. Matthias Templ, 2023. "Enhancing Precision in Large-Scale Data Analysis: An Innovative Robust Imputation Algorithm for Managing Outliers and Missing Values," Mathematics, MDPI, vol. 11(12), pages 1-22, June.
    15. Stefan Van Aelst & Gert Willems, 2010. "Inference for robust canonical variate analysis," 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. 4(2), pages 181-197, September.
    16. You-Gan Wang & Xu Lin, 2005. "Effects of Variance-Function Misspecification in Analysis of Longitudinal Data," Biometrics, The International Biometric Society, vol. 61(2), pages 413-421, June.
    17. J. L. Scealy & A. H. Welsh, 2017. "A Directional Mixed Effects Model for Compositional Expenditure Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(517), pages 24-36, January.
    18. Sanjoy K. Sinha, 2019. "Robust small area estimation in generalized linear mixed models," METRON, Springer;Sapienza Università di Roma, vol. 77(3), pages 201-225, December.
    19. Gottard, Anna & Pacillo, Simona, 2010. "Robust concentration graph model selection," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3070-3079, December.
    20. Andreas Alfons & Wolfgang Baaske & Peter Filzmoser & Wolfgang Mader & Roland Wieser, 2011. "Robust variable selection with application to quality of life research," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 20(1), pages 65-82, March.

    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:124:y:2018:i:c:p:117-131. 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.