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Local influence in estimating equations

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  • Venezuela, Maria Kelly
  • Sandoval, Mônica Carneiro
  • Botter, Denise Aparecida

Abstract

Local 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.

Suggested Citation

  • Venezuela, Maria Kelly & Sandoval, Mônica Carneiro & Botter, Denise Aparecida, 2011. "Local influence in estimating equations," Computational Statistics & Data Analysis, Elsevier, vol. 55(4), pages 1867-1883, April.
    • Handle: RePEc:eee:csdana:v:55:y:2011:i:4:p:1867-1883
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    References listed on IDEAS

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    1. Hongtu Zhu & Joseph G. Ibrahim & Niansheng Tang & Heping Zhang, 2008. "Diagnostic measures for empirical likelihood of general estimating equations," Biometrika, Biometrika Trust, vol. 95(2), pages 489-507.
    2. 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.
    3. N. G. Cadigan & P. J. Farrell, 2002. "Generalized local influence with applications to fish stock cohort analysis," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 51(4), pages 469-483, October.
    4. Osorio, Felipe & Paula, Gilberto A. & Galea, Manuel, 2007. "Assessment of local influence in elliptical linear models with longitudinal structure," Computational Statistics & Data Analysis, Elsevier, vol. 51(9), pages 4354-4368, May.
    5. Rinaldo Artes & Bent Jørgensen, 2000. "Longitudinal Data Estimating Equations for Dispersion Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 27(2), pages 321-334, June.
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    Cited by:

    1. Aline B. Tsuyuguchi & Gilberto A. Paula & Michelli Barros, 2020. "Analysis of correlated Birnbaum–Saunders data based on estimating equations," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(3), pages 661-681, September.
    2. 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.
    3. Manghi, Roberto F. & Cysneiros, Francisco José A. & Paula, Gilberto A., 2019. "Generalized additive partial linear models for analyzing correlated data," Computational Statistics & Data Analysis, Elsevier, vol. 129(C), pages 47-60.

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