IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v59y2013icp70-81.html
   My bibliography  Save this article

Bootstrapping for highly unbalanced clustered data

Author

Listed:
  • Samanta, Mayukh
  • Welsh, A.H.

Abstract

We apply the generalized cluster bootstrap to both Gaussian quasi-likelihood and robust estimates in the context of highly unbalanced clustered data. We compare it with the transformation bootstrap where the data are generated by the random effect and transformation models and all the random variables have different distributions. We also develop a fast approach (proposed by Salibian-Barrera et al. (2008)) and show that it produces some encouraging results. We show that the generalized bootstrap performs better than the transformation bootstrap for highly unbalanced clustered data. We apply the generalized cluster bootstrap to a sample of income data for Australian workers.

Suggested Citation

  • Samanta, Mayukh & Welsh, A.H., 2013. "Bootstrapping for highly unbalanced clustered data," Computational Statistics & Data Analysis, Elsevier, vol. 59(C), pages 70-81.
    • Handle: RePEc:eee:csdana:v:59:y:2013:i:c:p:70-81
    DOI: 10.1016/j.csda.2012.09.004
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167947312003246
    Download Restriction: Full text for ScienceDirect subscribers only.
    File URL: https://libkey.io/10.1016/j.csda.2012.09.004?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. 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.
    2. 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.
    3. 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.
    4. 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. 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.
    2. 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.
    3. 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.
    4. 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.

    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. 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.
    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. 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.
    4. 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.
    5. Alfons, Andreas & Croux, Christophe & Gelper, Sarah, 2016. "Robust groupwise least angle regression," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 421-435.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. Riani, Marco & Atkinson, Anthony C., 2010. "Robust model selection with flexible trimming," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3300-3312, December.
    11. 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.
    12. 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.
    13. Gottard, Anna & Pacillo, Simona, 2010. "Robust concentration graph model selection," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3070-3079, December.
    14. 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.
    15. Wen Shi & Xi Chen & Jennifer Shang, 2019. "An Efficient Morris Method-Based Framework for Simulation Factor Screening," INFORMS Journal on Computing, INFORMS, vol. 31(4), pages 745-770, October.
    16. David Roodman & James G. MacKinnon & Morten Ørregaard Nielsen & Matthew D. Webb, 2019. "Fast and wild: Bootstrap inference in Stata using boottest," Stata Journal, StataCorp LP, vol. 19(1), pages 4-60, March.
    17. repec:cep:stiecm:/2014/572 is not listed on IDEAS
    18. Lorenzo Camponovo & Taisuke Otsu, 2012. "Breakdown point theory for implied probability bootstrap," Econometrics Journal, Royal Economic Society, vol. 15(1), pages 32-55, February.
    19. Coisnon, Thomas & Rousselière, Damien & Rousselière, Samira, 2018. "Information on biodiversity and environmental behaviors: a European study of individual and institutional drivers to adopt sustainable gardening practices," Working Papers 272611, Institut National de la recherche Agronomique (INRA), Departement Sciences Sociales, Agriculture et Alimentation, Espace et Environnement (SAE2).
    20. Robin Van Oirbeek & Emmanuel Lesaffre, 2018. "An Investigation of the Discriminatory Ability of the Clustering Effect of the Frailty Survival Model," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 6(3), pages 87-98, April.
    21. Lorenzo Camponovo & Taisuke Otsu, 2015. "Robustness of Bootstrap in Instrumental Variable Regression," Econometric Reviews, Taylor & Francis Journals, vol. 34(3), pages 352-393, 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:59:y:2013:i:c:p:70-81. 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.