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Fitting General Linear Model for Longitudinal Survey Data under Informative Sampling

Abdulhakeem A. H. Eideh ()

Statistics in Transition new series, 2010, vol. 11, issue 3, 517-538

Abstract: The purpose of this article is to account for informative sampling in fitting superpopulation model for multivariate observations, and in particular multivariate normal distribution, for longitudinal survey data. The idea behind the proposed approach is to extract the model holding for the sample data as a function of the model in the population and the first order inclusion probabilities, and then fit the sample model using maximum likelihood, pseudo maximum likelihood and estimating equations methods. As an application of the results, we fit the general linear model for longitudinal survey data under informative sampling using different covariance structures: the exponential correlation model, the uniform correlation model, and the random effect model, and using different conditional expectations of first order inclusion probabilities given the study variable. The main feature of the present estimators is their behaviours in terms of the informativeness parameters.

Keywords: General Linear Model; Informative sampling; Longitudinal Survey Data; Maximum Likelihood; and Sample distribution (search for similar items in EconPapers)
Date: 2010
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Handle: RePEc:csb:stintr:v:11:y:2010:i:3:p:517-538