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Joint inference of misaligned irregular time series with application to Greenland ice core data
Date Issued
2015-03-25
Date Available
2016-09-19T14:57:03Z
Abstract
Ice cores provide insight into the past climate over many millennia. Due to ice compaction, the raw data for any single core are irregular in time. Multiple cores have different irregularities; and when considered together, they are misaligned in time. After processing, such data are made available to researchers as regular time series: a data product. Typically, these cores are independently processed. This paper considers a fast Bayesian method for the joint processing of multiple irregular series. This is shown to be more efficient than the independent alternative. Furthermore, our explicit framework permits a reliable modelling of the impact of the multiple sources of uncertainty. The methodology is illustrated with the analysis of a pair of ice cores. Our data products, in the form of posterior marginals or joint distributions on an arbitrary temporal grid, are finite Gaussian mixtures. We can also produce process histories to study non-linear functionals of interest. More generally, the concept of joint analysis via hierarchical Gaussian process models can be widely extended, as the models used can be viewed within the larger context of continuous space–time processes.
Sponsorship
Science Foundation Ireland
Type of Material
Journal Article
Publisher
Copernicus Publications
Journal
Advances in Statistical Climatology, Meteorology and Oceanography
Volume
1
Issue
1
Start Page
15
End Page
27
Copyright (Published Version)
2015 the Authors
Language
English
Status of Item
Peer reviewed
This item is made available under a Creative Commons License
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