Robust estimating functions and bias correction for longitudinal data analysis


Autoria(s): Wang, Y-G.; Lin, X.; Zhu, M.
Data(s)

2005

Resumo

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.

Identificador

http://eprints.qut.edu.au/90495/

Publicador

Wiley-Blackwell Publishing Ltd

Relação

DOI:10.1111/j.1541-0420.2005.00354.x

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

Direitos

Copyright © 2005 John Wiley & Sons, Inc

Fonte

Science & Engineering Faculty

Palavras-Chave #bias #estimating functions #longitudinal data #M-estimation #robust #estimation #estimating equations #models #regression #likelihood
Tipo

Journal Article