On the properties of GEE estimators in the presence of invariant covariates (1996)
| Venue: | Biometrical J |
| Citations: | 2 - 1 self |
BibTeX
@ARTICLE{Spiess96onthe,
author = {Martin Spiess and Alfred Hamerle},
title = {On the properties of GEE estimators in the presence of invariant covariates},
journal = {Biometrical J},
year = {1996},
volume = {38},
pages = {931--940}
}
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Abstract
In this paper it is shown that the use of non-singular block invariant matrices of covariates leads to `generalized estimating equations' estimators (GEE estimators; Liang, K.-Y. & Zeger, S. (1986). Biometrika, 73(1), 13--22) which are identical regardless of the `working' correlation matrix used. Moreover, they are efficient (McCullagh, P. (1983). The Annals of Statistics, 11(1), 59--67). If on the other hand only time invariant covariates are used the efficiency gain in choosing the `correct' vs. an `incorrect' correlation structure is shown to be negligible. The results of a simple simulation study suggest that although different GEE estimators are no more identical and are no more as efficient as an ML estimator, the differences are still negligible if both time and block invariant covariates are present. Key words: Generalized estimating equations; Invariant covariates; Asymptotic properties. 1 Introduction The `generalized estimating equations' approach (GEE approach) proposed...
