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dc.contributor.authorWang, Wen-Tingen_US
dc.contributor.authorHuang, Hsin-Chengen_US
dc.date.accessioned2018-08-21T05:53:25Z-
dc.date.available2018-08-21T05:53:25Z-
dc.date.issued2018-03-01en_US
dc.identifier.issn1180-4009en_US
dc.identifier.urihttp://dx.doi.org/10.1002/env.2481en_US
dc.identifier.urihttp://hdl.handle.net/11536/144669-
dc.description.abstractIn climate and atmospheric research, many phenomena involve more than one spatial processes covarying in space. To understand how one process is affected by another, maximum covariance analysis is commonly applied. However, the patterns obtained from maximum covariance analysis may sometimes be difficult to interpret. In this paper, we propose a regularization approach to promote spatial features in dominant coupled patterns by introducing smoothness and sparseness penalties while accounting for their orthogonalities. We develop an efficient algorithm to solve the resulting optimization problem by using the alternating direction method of multipliers. The effectiveness of the proposed method is illustrated by several numerical examples, including an application to study how precipitation in East Africa is affected by sea surface temperatures in the Indian Ocean.en_US
dc.language.isoen_USen_US
dc.subjectalternating direction method of multipliersen_US
dc.subjectLassoen_US
dc.subjectsingular value decompositionen_US
dc.subjectsmoothing splinesen_US
dc.subjectorthogonal constrainten_US
dc.titleRegularized spatial maximum covariance analysisen_US
dc.typeArticleen_US
dc.identifier.doi10.1002/env.2481en_US
dc.identifier.journalENVIRONMETRICSen_US
dc.citation.volume29en_US
dc.contributor.department統計學研究所zh_TW
dc.contributor.departmentInstitute of Statisticsen_US
dc.identifier.wosnumberWOS:000427247200001en_US
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