Joint inference of misaligned irregular time series with application to Greenland ice core data
|Title:||Joint inference of misaligned irregular time series with application to Greenland ice core data||Authors:||Doan, Thinh K.
Parnell, Andrew C.
|Permanent link:||http://hdl.handle.net/10197/7964||Date:||25-Mar-2015||Online since:||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.||Funding Details:||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||Keywords:||Climate data; Greenland; Joint modelling; Bayesian inference||DOI:||10.5194/ascmo-1-15-2015||Language:||en||Status of Item:||Peer reviewed|
|Appears in Collections:||Mathematics and Statistics Research Collection|
Earth Institute Research Collection
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