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dc.contributor.authorEideh, Abdulhakeem A.H.
dc.date.accessioned2018-09-02T19:56:05Z
dc.date.available2018-09-02T19:56:05Z
dc.date.issued2010-12-10
dc.identifier.issn2450-0291
dc.identifier.urihttps://dspace.alquds.edu/handle/20.500.12213/798
dc.description.abstractThe 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.en_US
dc.language.isoen_USen_US
dc.subjectGeneral Linear Modelen_US
dc.subjectInformative samplingen_US
dc.subjectLongitudinal Survey Dataen_US
dc.subjectMaximum Likelihooden_US
dc.subjectSample distributionen_US
dc.titleFitting general linear model for longitudinal survey data under informative samplingen_US
dc.typeArticleen_US


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