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Indices for covariance mis-specification in longitudinal data analysis with no missing responses and with MAR drop-outs

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  • Hines, R.J. O'Hara
  • Hines, W.G.S.

Abstract

Mis-specification of the covariance structure in longitudinal data can result in loss of regression estimation efficiency and in misleading influence diagnostics. Therefore, a rule-of-thumb, even one that is rough, for detecting covariance mis-specification would prove valuable to data analysts. In this paper, we examine two indices for detecting the mis-specification of the covariance structure of longitudinal normal, Poisson or binary responses. Our work shows that the suggested indices prove to be worthwhile when there are no missing time observations; they, however, should be used with caution when there are MAR drop-outs.

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  • 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.
    • Handle: RePEc:eee:csdana:v:54:y:2010:i:4:p:806-815
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    1. Xu, Jianwen & Wang, You-Gan, 2014. "Intra-cluster correlation structure in longitudinal data analysis: Selection criteria and misspecification tests," Computational Statistics & Data Analysis, Elsevier, vol. 80(C), pages 70-77.

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