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The effect of the working correlation on fitting models to longitudinal data

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  • Samuel Muller
  • Suojin Wang
  • A. H. Welsh

Abstract

We present a detailed discussion of the theoretical properties of quadratic inference function estimators of the parameters in marginal linear regression models. We consider the effect of the choice of working correlation on fundamental questions including the existence of quadratic inference function estimators, their relationship with generalized estimating equations estimators, and the robustness and asymptotic relative efficiency of quadratic inference function and generalized estimating equations estimators. We show that the quadratic inference function estimators do not always exist and propose a way to handle this. We then show that they have unbounded influence functions and can be more or less asymptotically efficient than generalized estimating equations estimators. We also present empirical evidence to demonstrate these results. We conclude that the choice of working correlation can have surprisingly large effects.

Suggested Citation

  • Samuel Muller & Suojin Wang & A. H. Welsh, 2024. "The effect of the working correlation on fitting models to longitudinal data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 51(2), pages 891-912, June.
    • Handle: RePEc:bla:scjsta:v:51:y:2024:i:2:p:891-912
    DOI: 10.1111/sjos.12704
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    References listed on IDEAS

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