IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v57y2001i4p1198-1206.html
   My bibliography  Save this article

Small-Sample Adjustments for Wald-Type Tests Using Sandwich Estimators

Author

Listed:
  • Michael P. Fay
  • Barry I. Graubard

Abstract

No abstract is available for this item.

Suggested Citation

  • Michael P. Fay & Barry I. Graubard, 2001. "Small-Sample Adjustments for Wald-Type Tests Using Sandwich Estimators," Biometrics, The International Biometric Society, vol. 57(4), pages 1198-1206, December.
    • Handle: RePEc:bla:biomet:v:57:y:2001:i:4:p:1198-1206
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/j.0006-341X.2001.01198.x
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    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. Chesher, Andrew & Austin, Gerard, 1991. "The finite-sample distributions of heteroskedasticity robust Wald statistics," Journal of Econometrics, Elsevier, vol. 47(1), pages 153-173, January.
    2. Lloyd A. Mancl & Timothy A. DeRouen, 2001. "A Covariance Estimator for GEE with Improved Small‐Sample Properties," Biometrics, The International Biometric Society, vol. 57(1), pages 126-134, March.
    3. John S. Preisser & Bahjat F. Qaqish, 1999. "Robust Regression for Clustered Data with Application to Binary Responses," Biometrics, The International Biometric Society, vol. 55(2), pages 574-579, 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. Masahiko Gosho & Hisashi Noma & Kazushi Maruo, 2021. "Practical Review and Comparison of Modified Covariance Estimators for Linear Mixed Models in Small‐sample Longitudinal Studies with Missing Data," International Statistical Review, International Statistical Institute, vol. 89(3), pages 550-572, December.
    2. Olli Saarela & David A. Stephens & Erica E. M. Moodie & Marina B. Klein, 2015. "On Bayesian estimation of marginal structural models," Biometrics, The International Biometric Society, vol. 71(2), pages 279-288, June.
    3. Slawa Rokicki & Jessica Cohen & Gunther Fink & Joshua Salomon & Mary Beth Landrum, 2018. "Inference with difference-in-differences with a small number of groups: a review, simulation study and empirical application using SHARE data," CHaRMS Working Papers 18-01, Centre for HeAlth Research at the Management School (CHaRMS).
    4. Haiyan Wang & Michael Akritas, 2010. "Inference from heteroscedastic functional data," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 22(2), pages 149-168.
    5. Steven Teerenstra & Bing Lu & John S. Preisser & Theo van Achterberg & George F. Borm, 2010. "Sample Size Considerations for GEE Analyses of Three-Level Cluster Randomized Trials," Biometrics, The International Biometric Society, vol. 66(4), pages 1230-1237, December.
    6. Barry I. Graubard & Thomas R. Fears, 2005. "Standard Errors for Attributable Risk for Simple and Complex Sample Designs," Biometrics, The International Biometric Society, vol. 61(3), pages 847-855, September.
    7. Alexander Robitzsch, 2023. "Linking Error in the 2PL Model," J, MDPI, vol. 6(1), pages 1-27, January.
    8. Bing Lu & John S. Preisser & Bahjat F. Qaqish & Chirayath Suchindran & Shrikant I. Bangdiwala & Mark Wolfson, 2007. "A Comparison of Two Bias-Corrected Covariance Estimators for Generalized Estimating Equations," Biometrics, The International Biometric Society, vol. 63(3), pages 935-941, September.
    9. Ji-Hyun Lee & Michael J Schell & Richard Roetzheim, 2009. "Analysis of Group Randomized Trials with Multiple Binary Endpoints and Small Number of Groups," PLOS ONE, Public Library of Science, vol. 4(10), pages 1-9, October.
    10. Tang, Nian-Sheng & Tang, Man-Lai & Qiu, Shi-Fang, 2008. "Testing the equality of proportions for correlated otolaryngologic data," Computational Statistics & Data Analysis, Elsevier, vol. 52(7), pages 3719-3729, March.
    11. Galea, Manuel & de Castro, Mário, 2017. "Robust inference in a linear functional model with replications using the t distribution," Journal of Multivariate Analysis, Elsevier, vol. 160(C), pages 134-145.
    12. Philip M. Westgate & Woodrow W. Burchett, 2017. "A Comparison of Correlation Structure Selection Penalties for Generalized Estimating Equations," The American Statistician, Taylor & Francis Journals, vol. 71(4), pages 344-353, October.
    13. Hsiu-Ting Yu & Mark Rooij, 2013. "Model Selection for the Trend Vector Model," Journal of Classification, Springer;The Classification Society, vol. 30(3), pages 338-369, October.

    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. A. Colin Cameron & Jonah B. Gelbach & Douglas L. Miller, 2008. "Bootstrap-Based Improvements for Inference with Clustered Errors," The Review of Economics and Statistics, MIT Press, vol. 90(3), pages 414-427, August.
    2. Hammill, Bradley G. & Preisser, John S., 2006. "A SAS/IML software program for GEE and regression diagnostics," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 1197-1212, November.
    3. Antoine, Bertille & Lavergne, Pascal, 2023. "Identification-robust nonparametric inference in a linear IV model," Journal of Econometrics, Elsevier, vol. 235(1), pages 1-24.
    4. Psaradakis, Zacharias & Sola, Martin, 1996. "On the power of tests for superexogeneity and structural invariance," Journal of Econometrics, Elsevier, vol. 72(1-2), pages 151-175.
    5. Richard H. Spady & Sami Stouli, 2018. "Simultaneous Mean-Variance Regression," Bristol Economics Discussion Papers 18/697, School of Economics, University of Bristol, UK.
    6. Preisser, John S. & Garcia, Daniel I., 2005. "Alternative computational formulae for generalized linear model diagnostics: identifying influential observations with SAS software," Computational Statistics & Data Analysis, Elsevier, vol. 48(4), pages 755-764, April.
    7. J. E. Mills & C. A. Field & D. J. Dupuis, 2002. "Marginally Specified Generalized Linear Mixed Models: A Robust Approach," Biometrics, The International Biometric Society, vol. 58(4), pages 727-734, December.
    8. Pötscher, Benedikt M. & Preinerstorfer, David, 2021. "Valid Heteroskedasticity Robust Testing," MPRA Paper 107420, University Library of Munich, Germany.
    9. Pötscher, Benedikt M. & Preinerstorfer, David, 2023. "How Reliable Are Bootstrap-Based Heteroskedasticity Robust Tests?," Econometric Theory, Cambridge University Press, vol. 39(4), pages 789-847, August.
    10. Zhang, Yuexia & Qin, Guoyou & Zhu, Zhongyi & Zhang, Jiajia, 2018. "Robust estimation in linear regression models for longitudinal data with covariate measurement errors and outliers," Journal of Multivariate Analysis, Elsevier, vol. 168(C), pages 261-275.
    11. Romano, Joseph P. & Wolf, Michael, 2017. "Resurrecting weighted least squares," Journal of Econometrics, Elsevier, vol. 197(1), pages 1-19.
    12. Westgate, Philip M., 2013. "A bias-corrected covariance estimator for improved inference when using an unstructured correlation with quadratic inference functions," Statistics & Probability Letters, Elsevier, vol. 83(6), pages 1553-1558.
    13. You-Gan Wang & Yuning Zhao, 2007. "A Modified Pseudolikelihood Approach for Analysis of Longitudinal Data," Biometrics, The International Biometric Society, vol. 63(3), pages 681-689, September.
    14. Haiyan Wang & Michael Akritas, 2010. "Inference from heteroscedastic functional data," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 22(2), pages 149-168.
    15. Paniagua, Victoria, 2022. "When clients vote for brokers: How elections improve public goods provision in urban slums," World Development, Elsevier, vol. 158(C).
    16. Galea, Manuel & de Castro, Mário, 2017. "Robust inference in a linear functional model with replications using the t distribution," Journal of Multivariate Analysis, Elsevier, vol. 160(C), pages 134-145.
    17. Michael O'Hara & Christopher F. Parmeter, 2013. "Nonparametric Generalized Least Squares in Applied Regression Analysis," Pacific Economic Review, Wiley Blackwell, vol. 18(4), pages 456-474, October.
    18. You-Gan Wang & Xu Lin & Min Zhu, 2005. "Robust Estimating Functions and Bias Correction for Longitudinal Data Analysis," Biometrics, The International Biometric Society, vol. 61(3), pages 684-691, September.
    19. Liu, Anna & Qin, Li & Staudenmayer, John, 2010. "M-type smoothing spline ANOVA for correlated data," Journal of Multivariate Analysis, Elsevier, vol. 101(10), pages 2282-2296, November.
    20. Michael Eichinger & Tatiana Görig & Sabine Georg & Dorle Hoffmann & Diana Sonntag & Heike Philippi & Jochem König & Michael S. Urschitz & Freia De Bock, 2022. "Evaluation of a Complex Intervention to Strengthen Participation-Centred Care for Children with Special Healthcare Needs: Protocol of the Stepped Wedge Cluster Randomised PART-CHILD Trial," IJERPH, MDPI, vol. 19(24), pages 1-18, December.

    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:biomet:v:57:y:2001:i:4:p:1198-1206. 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: http://www.blackwellpublishing.com/journal.asp?ref=0006-341X .

    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.