An Approximation of a Longitudinal Stochastic Model
Date
2019-03-25
Authors
Salah, Khalid A.
Journal Title
Journal ISSN
Volume Title
Publisher
ClinMed International Library
Abstract
We 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.
Description
Keywords
Stochastic process , Longitudinal , Integrated ornsteinuhlenbeck , Bayesian , Spatial , Convolution
Citation
Salah KA (2019) An Approximation of a Longitudinal Stochastic Model. Int J Clin Biostat Biom 5:020. doi.org/10.23937/2469-5831/1510020