Detecting shifts in correlation and variability with application to ENSO-monsoon rainfall relationships
Summary This paper addresses the retrospective detection of step changes at unknown time points in the correlation structure of two or more climate times series. Both the variance of individual series and the covariance between series are addressed. For a sequence of vector-valued observations with...
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| Veröffentlicht in: | Theoretical and applied climatology 2008-11, Vol.94 (3-4), p.215-224 |
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| creator | Robinson, L. F. de la Peña, V. H. Kushnir, Y. |
| description | Summary
This paper addresses the retrospective detection of step changes at unknown time points in the correlation structure of two or more climate times series. Both the variance of individual series and the covariance between series are addressed. For a sequence of vector-valued observations with an approximate multivariate normal distribution, the proposed method is a parametric likelihood ratio test of the hypothesis of constant covariance against the hypothesis of at least one shift in covariance. The formulation of the test statistic and its asymptotic distribution are taken from Chen and Gupta (2000). This test is applied to the series comprised of the mean summer NINO3 index and the Indian monsoon rainfall index for the years 1871–2003. The most likely change point year was found to be 1980, with a resulting
p
-value of 0.12. The same test was applied to the series of NINO3 and Northeast Brazil rainfall observations from the years 1856–2001. A shift was detected in 1982 which is significant at the 1% level. Some or all of this shift in the covariance matrix can be attributed to a change in the variance of the Northeast Brazil rainfall. A variation of this methodology designed to increase power under certain multiple change point alternatives, specificallly when a shift is followed by a reversal, is also presented. Simulations to assess the power of the test under various alternatives are also included, in addition to a review of the literature on alternative methods. |
| doi_str_mv | 10.1007/s00704-007-0351-z |
| format | Article |
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This paper addresses the retrospective detection of step changes at unknown time points in the correlation structure of two or more climate times series. Both the variance of individual series and the covariance between series are addressed. For a sequence of vector-valued observations with an approximate multivariate normal distribution, the proposed method is a parametric likelihood ratio test of the hypothesis of constant covariance against the hypothesis of at least one shift in covariance. The formulation of the test statistic and its asymptotic distribution are taken from Chen and Gupta (2000). This test is applied to the series comprised of the mean summer NINO3 index and the Indian monsoon rainfall index for the years 1871–2003. The most likely change point year was found to be 1980, with a resulting
p
-value of 0.12. The same test was applied to the series of NINO3 and Northeast Brazil rainfall observations from the years 1856–2001. A shift was detected in 1982 which is significant at the 1% level. Some or all of this shift in the covariance matrix can be attributed to a change in the variance of the Northeast Brazil rainfall. A variation of this methodology designed to increase power under certain multiple change point alternatives, specificallly when a shift is followed by a reversal, is also presented. Simulations to assess the power of the test under various alternatives are also included, in addition to a review of the literature on alternative methods.</description><identifier>ISSN: 0177-798X</identifier><identifier>EISSN: 1434-4483</identifier><identifier>DOI: 10.1007/s00704-007-0351-z</identifier><language>eng</language><publisher>Vienna: Springer-Verlag</publisher><subject>Aquatic Pollution ; Atmospheric Protection/Air Quality Control/Air Pollution ; Atmospheric Sciences ; Climate ; Climatology ; Climatology. Bioclimatology. Climate change ; Earth and Environmental Science ; Earth Sciences ; Earth, ocean, space ; El Nino ; Exact sciences and technology ; External geophysics ; Geophysics. Techniques, methods, instrumentation and models ; Meteorology ; Monsoons ; Rain ; Rainfall index ; Statistical methods ; Time series ; Variance analysis ; Waste Water Technology ; Water Management ; Water Pollution Control</subject><ispartof>Theoretical and applied climatology, 2008-11, Vol.94 (3-4), p.215-224</ispartof><rights>Springer-Verlag 2008</rights><rights>2009 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c408t-f10e69b3c0b69cdb5ae5cca69f83befd378e6ae162c12a5c7b7f415d698383083</citedby><cites>FETCH-LOGICAL-c408t-f10e69b3c0b69cdb5ae5cca69f83befd378e6ae162c12a5c7b7f415d698383083</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00704-007-0351-z$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00704-007-0351-z$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>315,782,786,27931,27932,41495,42564,51326</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=21097138$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Robinson, L. F.</creatorcontrib><creatorcontrib>de la Peña, V. H.</creatorcontrib><creatorcontrib>Kushnir, Y.</creatorcontrib><title>Detecting shifts in correlation and variability with application to ENSO-monsoon rainfall relationships</title><title>Theoretical and applied climatology</title><addtitle>Theor Appl Climatol</addtitle><description>Summary
This paper addresses the retrospective detection of step changes at unknown time points in the correlation structure of two or more climate times series. Both the variance of individual series and the covariance between series are addressed. For a sequence of vector-valued observations with an approximate multivariate normal distribution, the proposed method is a parametric likelihood ratio test of the hypothesis of constant covariance against the hypothesis of at least one shift in covariance. The formulation of the test statistic and its asymptotic distribution are taken from Chen and Gupta (2000). This test is applied to the series comprised of the mean summer NINO3 index and the Indian monsoon rainfall index for the years 1871–2003. The most likely change point year was found to be 1980, with a resulting
p
-value of 0.12. The same test was applied to the series of NINO3 and Northeast Brazil rainfall observations from the years 1856–2001. A shift was detected in 1982 which is significant at the 1% level. Some or all of this shift in the covariance matrix can be attributed to a change in the variance of the Northeast Brazil rainfall. A variation of this methodology designed to increase power under certain multiple change point alternatives, specificallly when a shift is followed by a reversal, is also presented. Simulations to assess the power of the test under various alternatives are also included, in addition to a review of the literature on alternative methods.</description><subject>Aquatic Pollution</subject><subject>Atmospheric Protection/Air Quality Control/Air Pollution</subject><subject>Atmospheric Sciences</subject><subject>Climate</subject><subject>Climatology</subject><subject>Climatology. Bioclimatology. Climate change</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>Earth, ocean, space</subject><subject>El Nino</subject><subject>Exact sciences and technology</subject><subject>External geophysics</subject><subject>Geophysics. Techniques, methods, instrumentation and models</subject><subject>Meteorology</subject><subject>Monsoons</subject><subject>Rain</subject><subject>Rainfall index</subject><subject>Statistical methods</subject><subject>Time series</subject><subject>Variance analysis</subject><subject>Waste Water Technology</subject><subject>Water Management</subject><subject>Water Pollution Control</subject><issn>0177-798X</issn><issn>1434-4483</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2008</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kU1rFTEUhkNR8Fr9Ae5CwbqKnkwy-VhKWz-g2IUK7kImN3ObMjcZk9yW9teby7QVhLp5T3LynJccXoTeUHhPAeSH0gQ4aUqA9ZTcHaAV5YwTzhV7hlZApSRSq18v0MtSrgCgE0Ku0ObUV-9qiBtcLsNYCw4Ru5Szn2wNKWIb1_ja5mCHMIV6i29CvcR2nqfgFqAmfPbt-wXZplhSu2cb4minCT9YNN-5vELPW7P41_f1EP38dPbj5As5v_j89eTjOXEcVCUjBS_0wBwMQrv10FvfO2eFHhUb_LhmUnlhPRWdo53tnRzkyGm_FloxxUCxQ_Ru8Z1z-r3zpZptKM5Pk40-7YqRnAuhgOtGHv-XpLrXndCygUf_gFdpl2PbwnTQd1w2aRBdIJdTKdmPZs5ha_OtoWD2CZklIbM_7hMyd23m7b2xLc5OY7bRhfI42FHQkrL9Tt3ClfYUNz7__cDT5n8A0x-ijQ</recordid><startdate>20081101</startdate><enddate>20081101</enddate><creator>Robinson, L. 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F. ; de la Peña, V. H. ; Kushnir, Y.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c408t-f10e69b3c0b69cdb5ae5cca69f83befd378e6ae162c12a5c7b7f415d698383083</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2008</creationdate><topic>Aquatic Pollution</topic><topic>Atmospheric Protection/Air Quality Control/Air Pollution</topic><topic>Atmospheric Sciences</topic><topic>Climate</topic><topic>Climatology</topic><topic>Climatology. Bioclimatology. Climate change</topic><topic>Earth and Environmental Science</topic><topic>Earth Sciences</topic><topic>Earth, ocean, space</topic><topic>El Nino</topic><topic>Exact sciences and technology</topic><topic>External geophysics</topic><topic>Geophysics. Techniques, methods, instrumentation and models</topic><topic>Meteorology</topic><topic>Monsoons</topic><topic>Rain</topic><topic>Rainfall index</topic><topic>Statistical methods</topic><topic>Time series</topic><topic>Variance analysis</topic><topic>Waste Water Technology</topic><topic>Water Management</topic><topic>Water Pollution Control</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Robinson, L. F.</creatorcontrib><creatorcontrib>de la Peña, V. 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F.</au><au>de la Peña, V. H.</au><au>Kushnir, Y.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Detecting shifts in correlation and variability with application to ENSO-monsoon rainfall relationships</atitle><jtitle>Theoretical and applied climatology</jtitle><stitle>Theor Appl Climatol</stitle><date>2008-11-01</date><risdate>2008</risdate><volume>94</volume><issue>3-4</issue><spage>215</spage><epage>224</epage><pages>215-224</pages><issn>0177-798X</issn><eissn>1434-4483</eissn><abstract>Summary
This paper addresses the retrospective detection of step changes at unknown time points in the correlation structure of two or more climate times series. Both the variance of individual series and the covariance between series are addressed. For a sequence of vector-valued observations with an approximate multivariate normal distribution, the proposed method is a parametric likelihood ratio test of the hypothesis of constant covariance against the hypothesis of at least one shift in covariance. The formulation of the test statistic and its asymptotic distribution are taken from Chen and Gupta (2000). This test is applied to the series comprised of the mean summer NINO3 index and the Indian monsoon rainfall index for the years 1871–2003. The most likely change point year was found to be 1980, with a resulting
p
-value of 0.12. The same test was applied to the series of NINO3 and Northeast Brazil rainfall observations from the years 1856–2001. A shift was detected in 1982 which is significant at the 1% level. Some or all of this shift in the covariance matrix can be attributed to a change in the variance of the Northeast Brazil rainfall. A variation of this methodology designed to increase power under certain multiple change point alternatives, specificallly when a shift is followed by a reversal, is also presented. Simulations to assess the power of the test under various alternatives are also included, in addition to a review of the literature on alternative methods.</abstract><cop>Vienna</cop><pub>Springer-Verlag</pub><doi>10.1007/s00704-007-0351-z</doi><tpages>10</tpages></addata></record> |
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| identifier | ISSN: 0177-798X |
| ispartof | Theoretical and applied climatology, 2008-11, Vol.94 (3-4), p.215-224 |
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| subjects | Aquatic Pollution Atmospheric Protection/Air Quality Control/Air Pollution Atmospheric Sciences Climate Climatology Climatology. Bioclimatology. Climate change Earth and Environmental Science Earth Sciences Earth, ocean, space El Nino Exact sciences and technology External geophysics Geophysics. Techniques, methods, instrumentation and models Meteorology Monsoons Rain Rainfall index Statistical methods Time series Variance analysis Waste Water Technology Water Management Water Pollution Control |
| title | Detecting shifts in correlation and variability with application to ENSO-monsoon rainfall relationships |
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