Robust estimation of a spatiotemporal multilevel model with structural change. - Esmenda, Mary Jame M. (Quezon City University of the Philippines, 2013)
Abstract (A generalized spatiotemporal multilevel model is postulated and estimated using the imbedded forward search algorithm and a mixed model-maximum likelihood estimation into the backfitting framework. The model also account for possible structural changes even during the volatile periods of outbreaks. The forward search algorithm guarantees robustness of the estimates, filtering the effect of the temporal structural changes in the estimation of the group-level covariate, the individual-level covariate and the spatial parameters. The use of the backfitting algorithm provides computation efficiency and fast convergence for the additive spatiotemporal multilevel model. Using simulation studies, the proposed hybrid estimation method is shown to be capable of producing robust estimates of the parameters even in the presence of structural changes induced for example by the temporal epidemic outbreak. The model also produces robust estimates even for small sample sizes and short time series, which is common in epidemiological setting).
Thesis ( M.S.) --University of the Philippines.