Optimal spatial aggregation of space–time models and applications

Andrew Gehman and William W.S. Wei

Computational Statistics & Data Analysis, 2020, vol. 145, issue C

Abstract: Cancers are serious health concerns for every country. In the U.S. various cancer data are collected, monitored, and studied by the American Cancer Society (ACS). Since the data involves both spatial and temporal components, space–time models are useful for their analyses. Often these data (such as cancer rates) from varying geographical or political areas will be aggregated spatially to correspond to larger regions for analysis at that spatial scale. Methods to compare spatial aggregation schemes and to identify the optimal spatial aggregation are introduced. Specifically, some useful theorems and algorithms to determine the aggregation scheme that results in the minimum aggregate model error will be given, and they are demonstrated using the U.S. ovarian cancer incidence.

Keywords: Aggregation algorithm; STARMA model; GSTARMA model; Optimality (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:145:y:2020:i:c:s0167947320300049

DOI: 10.1016/j.csda.2020.106913

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