Indices for covariance mis-specification in longitudinal data analysis with no missing responses and with MAR drop-outs
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...
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| Veröffentlicht in: | Computational statistics & data analysis 2010-04, Vol.54 (4), p.806-815 |
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| container_title | Computational statistics & data analysis |
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| creator | Hines, R.J. O’Hara Hines, W.G.S. |
| description | 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. |
| doi_str_mv | 10.1016/j.csda.2009.11.001 |
| format | Article |
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| subjects | Exact sciences and technology General topics Linear inference, regression Mathematics Multivariate analysis Numerical analysis Numerical analysis. Scientific computation Numerical methods in probability and statistics Probability and statistics Sciences and techniques of general use Statistics |
| title | Indices for covariance mis-specification in longitudinal data analysis with no missing responses and with MAR drop-outs |
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