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- Nalule Muhumuza, Rebecca, et al.
(författare)
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Determining Influential Factors in Spatio-temporal Models
- 2020
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Ingår i: Demography of Population Health, Aging and Health Expenditures. - Cham : Springer International Publishing. - 9783030446956 - 9783030446949 ; , s. 347-357
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Bokkapitel (refereegranskat)abstract
- In various areas of modern statistical applications such as in Environmetrics, Image Processing, Epidemiology, Biology, Astronomy, Industrial Mathematics, and many others, we encounter challenges of analyzing massive data sets which are spatially observable, often presented as maps, and temporally correlated. The analysis of such data is usually performed with the goal to obtain both the spatial interpolation and the temporal prediction. In both cases, the data-generating process has to be fitted by an appropriate stochastic model which should have two main properties: (i) it should provide a good fit to the true underlying model; (ii) its structure could not be too complicated avoiding considerable estimation error that appears by fitting the model to real data. Consequently, achieving the reasonable trade-off between the model uncertainty and the parameter uncertainty is one of the most difficult questions of modern statistical theory.We deal with this problem in the case of general spatio-temporal models by applying the LOESS predictor for both the spatial interpolation and the temporal prediction. The number of closest neighboring regions to be used in its construction is determined by cross-validation. We also discuss the computational aspects in the case of large-dimensional data and apply the theoretical findings to real data consisting of the number of influenza cases observed in the south of Germany.
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| 2. |
- Aye, Tin Nwe, 1989-, et al.
(författare)
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Increasing Efficiency in the EBT Algorithm
- 2020
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Ingår i: Demography of Population Health, Aging and Health Expenditures. - Cham : Springer. - 9783030446956 - 9783030446956 ; , s. 289-317
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Bokkapitel (refereegranskat)abstract
- The Escalator Boxcar Train (EBT) is a commonly used method for solving physiologically structured population models. The main goal of this paper is to overcome computational disadvantages of the EBT method. We prove convergence, for a general class of EBT models in which we modify the original EBT formulation, allowing merging of cohorts. We show that this modified EBT method induces a bounded number of cohorts, independent of the number of time steps. This in turn, improves the numerical algorithm from polynomial to linear time. An EBT simulation of the Daphnia model is used as an illustration of these findings.
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