title: Robust estimating functions and bias correction for longitudinal data analysis.
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.
authors.affiliation: CSIRO Mathematical and Information Sciences, 65 Brockway Road, Floreat, Western Australia 6014, Australia. You-Gan.Wang@csiro.au
authors.forename: You-Gan
authors.initials: YG
authors.lastname: Wang
authors.forename: Xu
authors.initials: X
authors.lastname: Lin
authors.forename: Min
authors.initials: M
authors.lastname: Zhu
mesh_terms: D000367:Age Factors; D000927:Anticonvulsants / Q000627:therapeutic use; D015982:Bias; D003627:Data Interpretation, Statistical; D004827:Epilepsy / Q000188:drug therapy; D006801:Humans; D008137:Longitudinal Studies; D016032:Randomized Controlled Trials as Topic / Q000379:methods*; D012640:Seizures / Q000517:prevention & control; D005680:gamma-Aminobutyric Acid / Q000031:analogs & derivatives / Q000627:therapeutic use
chemical_list: D000927:Anticonvulsants; C017985:progabide; D005680:gamma-Aminobutyric Acid