 Spatial Statistics for Distributional Data in Bayes Spaces: From Object-Oriented Kriging to the Analysis of Warping FunctionsAlessandra Menafoglio ()
A chapter in Advances in Compositional Data Analysis, 2021, pp 207-224 from Springer Abstract: Abstract In the presence of increasingly massive and heterogeneous spatial data, geostatistical modeling of distributional observations plays a key role. Choosing the “right” embedding space for these data is of paramount importance for their statistical processing, to account for their nature and inherent constraints. The Bayes space theory is a natural embedding space for (spatial) distributional data and was successfully applied in varied settings. The aim of this work is to review the state-of-the-art methods for spatial dependence modeling and prediction of distributional data, while shedding light on the strong links between Compositional Data Analysis, Functional Data Analysis, and, more generally, Object-Oriented Data Analysis, in the context of spatial statistics. We propose extensions of these methods to the multivariate setting, and discuss the applicability of the Bayes space approach to the spatial modeling of phase variability in Functional Data Analysis. Date: 2021 There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it. Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-71175-7_11 Ordering information: This item can be ordered from DOI: 10.1007/978-3-030-71175-7_11 Access Statistics for this chapter More chapters in Springer Books from Springer |
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