Please use this identifier to cite or link to this item: https://doi.org/10.1111/j.1541-0420.2005.00354.x
Title: Robust estimating functions and bias correction for longitudinal data analysis
Authors: Wang, Y.-G. 
Lin, X.
Zhu, M.
Keywords: Bias
Estimating functions
Longitudinal data
M-estimation
Robust estimation
Issue Date: Sep-2005
Citation: Wang, Y.-G., Lin, X., Zhu, M. (2005-09). Robust estimating functions and bias correction for longitudinal data analysis. Biometrics 61 (3) : 684-691+891. ScholarBank@NUS Repository. https://doi.org/10.1111/j.1541-0420.2005.00354.x
Abstract: 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.
Source Title: Biometrics
URI: http://scholarbank.nus.edu.sg/handle/10635/105336
ISSN: 0006341X
DOI: 10.1111/j.1541-0420.2005.00354.x
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