Local influence in estimating equations
AbstractLocal influence diagnostics based on estimating equations as the role of a gradient vector derived from any fit function are developed for repeated measures regression analysis. Our proposal generalizes tools used in other studies ([Cook, 1986] and [Cadigan and Farrell, 2002]), considering herein local influence diagnostics for a statistical model where estimation involves an estimating equation in which all observations are not necessarily independent of each other. Moreover, the measures of local influence are illustrated with some simulated data sets to assess influential observations. Applications using real data are presented.
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Bibliographic InfoArticle provided by Elsevier in its journal Computational Statistics & Data Analysis.
Volume (Year): 55 (2011)
Issue (Month): 4 (April)
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Web page: http://www.elsevier.com/locate/csda
Diagnostic techniques Local influence Generalized estimating equations Repeated measures Longitudinal data;
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- You-Gan Wang, 2003. "Working correlation structure misspecification, estimation and covariate design: Implications for generalised estimating equations performance," Biometrika, Biometrika Trust, vol. 90(1), pages 29-41, March.
- W.-Y. Poon & Y. S. Poon, 1999. "Conformal normal curvature and assessment of local influence," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(1), pages 51-61.
- Vens, Maren & Ziegler, Andreas, 2012. "Generalized estimating equations and regression diagnostics for longitudinal controlled clinical trials: A case study," Computational Statistics & Data Analysis, Elsevier, vol. 56(5), pages 1232-1242.
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