Robust estimating functions and bias correction for longitudinal data analysis

Wang, Y-G., Lin, X., & Zhu, Min (2005) Robust estimating functions and bias correction for longitudinal data analysis. Biometrics, 61(3), pp. 684-691.

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Robust methods are useful in making reliable statistical inferences when there are small deviations from the model assumptions. The widely used method of the generalized estimating equations can be "robustified" by replacing the standardized residuals with the M-residuals. If the Pearson residuals are assumed to be unbiased from zero, parameter estimators from the robust approach are asymptotically biased when error distributions are not symmetric. We propose a distribution-free method for correcting this bias. Our extensive numerical studies show that the proposed method can reduce the bias substantially. Examples are given for illustration.

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17 citations in Scopus
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17 citations in Web of Science®

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ID Code: 90495
Item Type: Journal Article
Refereed: Yes
Additional Information: ISI Document Delivery No.: 961VM
Times Cited: 16
Cited Reference Count: 21
Wang, YG Lin, X Zhu, M
Blackwell publishing
Keywords: bias, estimating functions, longitudinal data, M-estimation, robust, estimation, estimating equations, models, regression, likelihood
DOI: 10.1111/j.1541-0420.2005.00354.x
ISSN: 0006-341X
Divisions: Current > QUT Faculties and Divisions > QUT Business School
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Current > Schools > School of Economics & Finance
Copyright Owner: Copyright © 2005 John Wiley & Sons, Inc
Deposited On: 18 Nov 2015 03:25
Last Modified: 27 Sep 2016 01:32

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