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

Authors:
R.J. O'Hara Hines;W. G. S. Hines
Affiliations:
Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON N2L 3G1, Canada;Department of Mathematics and Statistics, University of Guelph, Guelph, ON N1G 2W1, Canada
Venue:
Computational Statistics & Data Analysis
Year:
2010

Citing 3
Cited 0

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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.