Analysis method for linear regression model with unequally spaced autoregression series error
The analysis method for regression model with unequally spaced time series error is presented, which is based on the relationship between the Green function of continuous system and the autoregression parameters of the time series. The conditional maximum likelihood estimation and exact maximum like...
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creator | Ma Xiaobing Chang Shihua |
description | The analysis method for regression model with unequally spaced time series error is presented, which is based on the relationship between the Green function of continuous system and the autoregression parameters of the time series. The conditional maximum likelihood estimation and exact maximum likelihood estimation of parameters of the regression model with unequally spaced correlated error are discussed in detail. The method is not only suitable for the time series with missing observations but also applicable to the irregularly sampled data in social and natural science. The method can also combine regression with autoregression and promote the precision of analysis and forecast. Numerical examples are given at last, which can illustrate the performance of the new method. |
doi_str_mv | 10.1109/PHM.2011.5939541 |
format | Conference Proceeding |
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The conditional maximum likelihood estimation and exact maximum likelihood estimation of parameters of the regression model with unequally spaced correlated error are discussed in detail. The method is not only suitable for the time series with missing observations but also applicable to the irregularly sampled data in social and natural science. The method can also combine regression with autoregression and promote the precision of analysis and forecast. 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The conditional maximum likelihood estimation and exact maximum likelihood estimation of parameters of the regression model with unequally spaced correlated error are discussed in detail. The method is not only suitable for the time series with missing observations but also applicable to the irregularly sampled data in social and natural science. The method can also combine regression with autoregression and promote the precision of analysis and forecast. Numerical examples are given at last, which can illustrate the performance of the new method.</description><subject>Analytical models</subject><subject>Irrigation</subject><subject>Linear regression model</subject><subject>Maximum likelihood estimation</subject><subject>Missing observation</subject><subject>Presses</subject><subject>Time series</subject><subject>Unequally spaced data</subject><issn>2166-563X</issn><issn>2166-5656</issn><isbn>9781424479511</isbn><isbn>1424479517</isbn><isbn>9781424479498</isbn><isbn>1424479509</isbn><isbn>9781424479504</isbn><isbn>1424479495</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2011</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpNkE9Lw0AUxNd_YKm5C172CyTu27zdZI-lqBUqeujBi5Td5MWupEndTZF8ewsW6Vzm8BuGYRi7BZEBCHP_tnjJpADIlMmNQjhjiSlKQIlYGDTlOZtI0DpVWumLU6YALv9Z_n7Nkhi_xEFalChwwj5mnW3H6CPf0rDpa970gbe-Ixt4oM9AMfq-49u-ppb_-GHD9x19723bjjzubEU1t_uhP4lGCp4ipxD6cMOuGttGSo4-ZavHh9V8kS5fn57ns2XqjRjSUtQlSCuQdAEKdO1ciZLQOnJFLqtCKQILFVknD9MdFg3YxjVY1a6SFeZTdvdX64lovQt-a8O4Pn6V_wI9IVs0</recordid><startdate>201105</startdate><enddate>201105</enddate><creator>Ma Xiaobing</creator><creator>Chang Shihua</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201105</creationdate><title>Analysis method for linear regression model with unequally spaced autoregression series error</title><author>Ma Xiaobing ; Chang Shihua</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-80d812a04e671516dbb842e4abeb732c755e1a1ceab2000b47f1afbf4cdbc2c43</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Analytical models</topic><topic>Irrigation</topic><topic>Linear regression model</topic><topic>Maximum likelihood estimation</topic><topic>Missing observation</topic><topic>Presses</topic><topic>Time series</topic><topic>Unequally spaced data</topic><toplevel>online_resources</toplevel><creatorcontrib>Ma Xiaobing</creatorcontrib><creatorcontrib>Chang Shihua</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Ma Xiaobing</au><au>Chang Shihua</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Analysis method for linear regression model with unequally spaced autoregression series error</atitle><btitle>2011 Prognostics and System Health Managment Confernece</btitle><stitle>PHM</stitle><date>2011-05</date><risdate>2011</risdate><spage>1</spage><epage>4</epage><pages>1-4</pages><issn>2166-563X</issn><eissn>2166-5656</eissn><isbn>9781424479511</isbn><isbn>1424479517</isbn><eisbn>9781424479498</eisbn><eisbn>1424479509</eisbn><eisbn>9781424479504</eisbn><eisbn>1424479495</eisbn><abstract>The analysis method for regression model with unequally spaced time series error is presented, which is based on the relationship between the Green function of continuous system and the autoregression parameters of the time series. The conditional maximum likelihood estimation and exact maximum likelihood estimation of parameters of the regression model with unequally spaced correlated error are discussed in detail. The method is not only suitable for the time series with missing observations but also applicable to the irregularly sampled data in social and natural science. The method can also combine regression with autoregression and promote the precision of analysis and forecast. Numerical examples are given at last, which can illustrate the performance of the new method.</abstract><pub>IEEE</pub><doi>10.1109/PHM.2011.5939541</doi><tpages>4</tpages></addata></record> |
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subjects | Analytical models Irrigation Linear regression model Maximum likelihood estimation Missing observation Presses Time series Unequally spaced data |
title | Analysis method for linear regression model with unequally spaced autoregression series error |
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