When used for modelling longitudinal data generalised estimating equations specify a working structure for the within-subject covariance matrices, aiming to produce efficient parameter estimators. However, misspecification of the working covariance structure may lead to a large loss of efficiency of the estimators of the mean parameters. In this paper we propose an approach for joint modelling of the mean and covariance structures of longitudinal data within the framework of generalised estimating equations. The resulting estimators for the mean and covariance parameters are shown to be consistent and asymptotically Normally distributed. Real data analysis and simulation studies show that the proposed approach yields efficient estimators fo...
This thesis focuses on the problem of estimating parameters in multivariate linear models where part...
The vast majority of structural equation models contain no mean structure, that is, the population m...
This paper proposes an extension of generalized linear models to the analysis of longitudinal data. ...
Mis-speci cation of covariance structure; Modelling of mean-covariance structures Mathematical Subje...
For longitudinal data, the within-subject dependence structure and covariance parameters may be of p...
E±cient estimation of the regression coe±cients in longitudinal data anal- ysis requires a correct s...
When the selected parametric model for the covariance structure is far from the true one, the corres...
The vast majority of structural equation models contain no mean structure, that is, the population m...
The method of generalised estimating equations for regression modelling of clustered outcomes allows...
The method of generalised estimating equations for regression modelling of clustered outcomes allows...
We investigate methods for data-based selection of working covariance models in the analysis of corr...
Abstract. Conventionally, in longitudinal studies, the mean structure has been thought to be more im...
The method of generalised estimating equations for regression modelling of clustered outcomes allows...
We investigate methods for data-based selection of working covariance models in the analysis of corr...
The method of generalised estimating equations for regression modelling of clustered outcomes allows...
This thesis focuses on the problem of estimating parameters in multivariate linear models where part...
The vast majority of structural equation models contain no mean structure, that is, the population m...
This paper proposes an extension of generalized linear models to the analysis of longitudinal data. ...
Mis-speci cation of covariance structure; Modelling of mean-covariance structures Mathematical Subje...
For longitudinal data, the within-subject dependence structure and covariance parameters may be of p...
E±cient estimation of the regression coe±cients in longitudinal data anal- ysis requires a correct s...
When the selected parametric model for the covariance structure is far from the true one, the corres...
The vast majority of structural equation models contain no mean structure, that is, the population m...
The method of generalised estimating equations for regression modelling of clustered outcomes allows...
The method of generalised estimating equations for regression modelling of clustered outcomes allows...
We investigate methods for data-based selection of working covariance models in the analysis of corr...
Abstract. Conventionally, in longitudinal studies, the mean structure has been thought to be more im...
The method of generalised estimating equations for regression modelling of clustered outcomes allows...
We investigate methods for data-based selection of working covariance models in the analysis of corr...
The method of generalised estimating equations for regression modelling of clustered outcomes allows...
This thesis focuses on the problem of estimating parameters in multivariate linear models where part...
The vast majority of structural equation models contain no mean structure, that is, the population m...
This paper proposes an extension of generalized linear models to the analysis of longitudinal data. ...