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

R.J. O'Hara Hines and W.G.S. Hines

Computational Statistics & Data Analysis, 2010, vol. 54, issue 4, 806-815

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.

Date: 2010
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