Spiess, Martin and Hamerle, Alfred (1996) On the properties of GEE estimators in the presence of invariant covariates. BIOMETRICAL JOURNAL, 38 (8). pp. 931-940. ISSN 0323-3847, 1521-4036
Full text not available from this repository.Abstract
Since LIANG and ZEGER (1986) proposed the 'generalized estimating equations' approach for the estimation of regression parameters in models with correlated discrete responses, a lot of work has been devoted to the investigation of the properties of the corresponding GEE estimators. However, the effects of different kinds of covariates have often been overlooked. In this paper it is shown that the use of non-singular block invariant matrices of covariates, as e.g. a design matrix in an analysis of variance model, leads to GEE estimators which are identical regardless of the 'working' correlation matrix used. Moreover, they are efficient (MCCULLAGH, 1983). If on the other hand only covariates are used which are invariant within blocks, the efficiency gain in choosing the 'correct' us. an 'incorrect' correlation structure is shown to be negligible. The results of a simple simulation study suggest that although different GEE estimators are not identical and are not as efficient as a ML estimator, the differences are still negligible if both types of invariant covariates are present.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | LONGITUDINAL DATA-ANALYSIS; BINARY DATA; LOGISTIC-REGRESSION; MODELS; DISCRETE; generalized estimating equations; invariant covariates; asymptotic properties; analysis of variance; correlated responses |
| Subjects: | 300 Social sciences > 330 Economics |
| Divisions: | Business, Economics and Information Systems > Institut für Betriebswirtschaftslehre > Entpflichtete oder im Ruhestand befindliche Professoren > Lehrstuhl für Statistik (Prof. Dr. Alfred Hamerle) |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 15 Nov 2023 06:55 |
| Last Modified: | 15 Nov 2023 06:55 |
| URI: | https://pred.uni-regensburg.de/id/eprint/52018 |
Actions (login required)
 |
View Item |
