An Approximation of a Longitudinal Stochastic Model

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dc.contributor.authorSalah, Khalid A.
dc.date.accessioned2019-11-06T10:56:58Z
dc.date.available2019-11-06T10:56:58Z
dc.date.issued2019-03-25
dc.description.abstractWe propose to approximate a model for repeated measures that incorporated random effects, correlated stochastic process and measurements error. The stochastic process used in this paper is the Integrated Ornstein-Uhlenbeck (IOU) process. We consider a Bayesian approach which is motivated by the complexity of the model, thus, we propose to approximate the IOU stochastic process into a continuous spatial model that constructed by convolving a very simple and independent, process with a kernel function. The goal of this approximation is to offer some advantages over specification through a spatial process of computing covariance, variogram, and extremal coefficient functions, also to add to the extremal coefficient plots the empirical estimates. This approximation is attractive because it facilitates calculations especially that contain a huge amount of data in addition it reduces the computational execution time, also it extends beyond simple stationary models.en_US
dc.identifier.citationSalah KA (2019) An Approximation of a Longitudinal Stochastic Model. Int J Clin Biostat Biom 5:020. doi.org/10.23937/2469-5831/1510020en_US
dc.identifier.issn2469-5831
dc.identifier.urihttps://dspace.alquds.edu/handle/20.500.12213/4855
dc.language.isoenen_US
dc.publisherClinMed International Libraryen_US
dc.subjectStochastic processen_US
dc.subjectLongitudinalen_US
dc.subjectIntegrated ornsteinuhlenbecken_US
dc.subjectBayesianen_US
dc.subjectSpatialen_US
dc.subjectConvolutionen_US
dc.titleAn Approximation of a Longitudinal Stochastic Modelen_US
dc.typeArticleen_US
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