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On repeated measures analysis with misspecified covariance structure

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  • Martin Crowder

Abstract

In recent years various sophisticated methods have been developed for the analysis of repeated measures, or longitudinal data. The more traditional approach, based on a normal likelihood function, has been shown to be unsatisfactory, in the sense of yielding asymptotically biased estimates when the covariance structure is misspecified. More recent methodology, based on generalized linear models and quasi‐likelihood estimation, has gained widespread acceptance as ‘generalized estimating equations’. However, this also has theoretical problems. In this paper a suggestion is made for improving the asymptotic behaviour of estimators by using the older approach, implemented via Gaussian estimation. The resulting estimating equations include the quasi‐score function as one component, so the methodology proposed can be viewed as a combination of Gaussian estimation and generalized estimating equations which has a firmer asymptotic basis than either alone has.

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  • Martin Crowder, 2001. "On repeated measures analysis with misspecified covariance structure," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(1), pages 55-62.
    • Handle: RePEc:bla:jorssb:v:63:y:2001:i:1:p:55-62
    DOI: 10.1111/1467-9868.00275
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    Cited by:

    1. Justine Shults & Ardythe L. Morrow, 2002. "Use of Quasi–Least Squares to Adjust for Two Levels of Correlation," Biometrics, The International Biometric Society, vol. 58(3), pages 521-530, September.
    2. Jinfeng Xu & Anthony Kuk, 2015. "On Pooling of Data and Its Relative Efficiency," International Statistical Review, International Statistical Institute, vol. 83(2), pages 309-323, August.
    3. Fu, Liya & Wang, You-Gan & Zhu, Min, 2015. "A Gaussian pseudolikelihood approach for quantile regression with repeated measurements," Computational Statistics & Data Analysis, Elsevier, vol. 84(C), pages 41-53.
    4. You-Gan Wang & Yuning Zhao, 2007. "A Modified Pseudolikelihood Approach for Analysis of Longitudinal Data," Biometrics, The International Biometric Society, vol. 63(3), pages 681-689, September.
    5. Hines, R.J. O'Hara & Hines, W.G.S., 2010. "Indices for covariance mis-specification in longitudinal data analysis with no missing responses and with MAR drop-outs," Computational Statistics & Data Analysis, Elsevier, vol. 54(4), pages 806-815, April.
    6. O'Hara Hines, R.J. & Hines, W.G.S., 2007. "Covariance miss-specification and the local influence approach in sensitivity analyses of longitudinal data with drop-outs," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 5537-5546, August.
    7. Hilal, Sawsan & Poon, Ser-Huang & Tawn, Jonathan, 2011. "Hedging the black swan: Conditional heteroskedasticity and tail dependence in S&P500 and VIX," Journal of Banking & Finance, Elsevier, vol. 35(9), pages 2374-2387, September.
    8. Gilks Walter R & Nye Tom M.W. & Lio Pietro, 2011. "A Variance-Components Model for Distance-Matrix Phylogenetic Reconstruction," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-36, March.

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