Model-Based Maximum Covariance Analysis for Irregularly Observed Climatological Data
In climatology, maximum covariance analysis (MCA) is one of the most popular tools for investigating association between two multivariate variables across time and space. These association studies are important because many climate phenomena such as the El Niño-Southern Oscillation (ENSO) and the No...
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ftanucanberra:oai:digitalcollections.anu.edu.au:1885/33815 2023-05-15T17:29:57+02:00 Model-Based Maximum Covariance Analysis for Irregularly Observed Climatological Data Salim, Agus Pawitan, Yudi 2015-12-08T22:26:51Z http://hdl.handle.net/1885/33815 unknown Allen Press Inc 1085-7117 http://hdl.handle.net/1885/33815 Journal of Agricultural, Biological, and Environmental Statistics Keywords: climate change climatology covariance analysis El Nino-Southern Oscillation Kalman filter North Atlantic Oscillation numerical model Climate change EM algorithm Singular value decomposition Southern oscillation Journal article 2015 ftanucanberra 2015-12-28T23:24:20Z In climatology, maximum covariance analysis (MCA) is one of the most popular tools for investigating association between two multivariate variables across time and space. These association studies are important because many climate phenomena such as the El Niño-Southern Oscillation (ENSO) and the North Atlantic Oscillation are results of interaction between these variables. Despite its popularity, maximum covariance analysis does not provide straightforward statistical inference on its estimates and furthermore it does not provide an objective way to handle irregularly observed data, frequently encountered in climatology. The aim of this article is to describe a model-based maximum covariance analysis that can accommodate irregularly observed data. The methodology combines maximum covariance analysis's relationship with Tucker inter-battery factor analysis and the state-space methodology for missing data. The methodology is illustrated with an application to investigate association between Irish winter precipitation and global sea surface temperature (SST) anomalies. Article in Journal/Newspaper North Atlantic North Atlantic oscillation Australian National University: ANU Digital Collections |
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Open Polar |
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Australian National University: ANU Digital Collections |
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unknown |
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Keywords: climate change climatology covariance analysis El Nino-Southern Oscillation Kalman filter North Atlantic Oscillation numerical model Climate change EM algorithm Singular value decomposition Southern oscillation |
spellingShingle |
Keywords: climate change climatology covariance analysis El Nino-Southern Oscillation Kalman filter North Atlantic Oscillation numerical model Climate change EM algorithm Singular value decomposition Southern oscillation Salim, Agus Pawitan, Yudi Model-Based Maximum Covariance Analysis for Irregularly Observed Climatological Data |
topic_facet |
Keywords: climate change climatology covariance analysis El Nino-Southern Oscillation Kalman filter North Atlantic Oscillation numerical model Climate change EM algorithm Singular value decomposition Southern oscillation |
description |
In climatology, maximum covariance analysis (MCA) is one of the most popular tools for investigating association between two multivariate variables across time and space. These association studies are important because many climate phenomena such as the El Niño-Southern Oscillation (ENSO) and the North Atlantic Oscillation are results of interaction between these variables. Despite its popularity, maximum covariance analysis does not provide straightforward statistical inference on its estimates and furthermore it does not provide an objective way to handle irregularly observed data, frequently encountered in climatology. The aim of this article is to describe a model-based maximum covariance analysis that can accommodate irregularly observed data. The methodology combines maximum covariance analysis's relationship with Tucker inter-battery factor analysis and the state-space methodology for missing data. The methodology is illustrated with an application to investigate association between Irish winter precipitation and global sea surface temperature (SST) anomalies. |
format |
Article in Journal/Newspaper |
author |
Salim, Agus Pawitan, Yudi |
author_facet |
Salim, Agus Pawitan, Yudi |
author_sort |
Salim, Agus |
title |
Model-Based Maximum Covariance Analysis for Irregularly Observed Climatological Data |
title_short |
Model-Based Maximum Covariance Analysis for Irregularly Observed Climatological Data |
title_full |
Model-Based Maximum Covariance Analysis for Irregularly Observed Climatological Data |
title_fullStr |
Model-Based Maximum Covariance Analysis for Irregularly Observed Climatological Data |
title_full_unstemmed |
Model-Based Maximum Covariance Analysis for Irregularly Observed Climatological Data |
title_sort |
model-based maximum covariance analysis for irregularly observed climatological data |
publisher |
Allen Press Inc |
publishDate |
2015 |
url |
http://hdl.handle.net/1885/33815 |
genre |
North Atlantic North Atlantic oscillation |
genre_facet |
North Atlantic North Atlantic oscillation |
op_source |
Journal of Agricultural, Biological, and Environmental Statistics |
op_relation |
1085-7117 http://hdl.handle.net/1885/33815 |
_version_ |
1766125190639517696 |