Calibration Weighting Methods for Complex Surveys
This paper presents a careful investigation of the three popular calibration weighting methods: (i) generalised regression; (ii) generalised exponential tilting and (iii) generalised pseudo empirical likelihood, with a major focus on computational aspects of the methods and some empirical evidences...
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| Veröffentlicht in: | International statistical review 2016-04, Vol.84 (1), p.79-98 |
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| container_title | International statistical review |
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| creator | Wu, Changbao Lu, Wilson W. |
| description | This paper presents a careful investigation of the three popular calibration weighting methods: (i) generalised regression; (ii) generalised exponential tilting and (iii) generalised pseudo empirical likelihood, with a major focus on computational aspects of the methods and some empirical evidences on calibrated weights. We also propose a simple weight trimming method for rangerestricted calibration. The finite sample behaviour of the weights obtained by the three calibration weighting methods and the effectiveness of the proposed weight trimming method are examined through limited simulation studies. |
| doi_str_mv | 10.1111/insr.12097 |
| format | Article |
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We also propose a simple weight trimming method for rangerestricted calibration. The finite sample behaviour of the weights obtained by the three calibration weighting methods and the effectiveness of the proposed weight trimming method are examined through limited simulation studies.</description><identifier>ISSN: 0306-7734</identifier><identifier>EISSN: 1751-5823</identifier><identifier>DOI: 10.1111/insr.12097</identifier><language>eng</language><publisher>Hoboken: Blackwell Publishing Ltd</publisher><subject>Calibration ; Computer simulation ; Empirical analysis ; Exponential tilting ; Kullback-Leibler distance ; Mathematical analysis ; Newton-Raphson procedure ; Polls & surveys ; pseudo empirical likelihood ; range-restricted calibration weights ; Regression ; Regression analysis ; regression weighting ; Samples ; Simulation ; Trimming ; Weight ; Weighting methods</subject><ispartof>International statistical review, 2016-04, Vol.84 (1), p.79-98</ispartof><rights>2016 International Statistical Institute</rights><rights>2015 The Authors. International Statistical Review © 2015 International Statistical Institute</rights><rights>Copyright John Wiley & Sons, Inc. 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We also propose a simple weight trimming method for rangerestricted calibration. The finite sample behaviour of the weights obtained by the three calibration weighting methods and the effectiveness of the proposed weight trimming method are examined through limited simulation studies.</description><subject>Calibration</subject><subject>Computer simulation</subject><subject>Empirical analysis</subject><subject>Exponential tilting</subject><subject>Kullback-Leibler distance</subject><subject>Mathematical analysis</subject><subject>Newton-Raphson procedure</subject><subject>Polls & surveys</subject><subject>pseudo empirical likelihood</subject><subject>range-restricted calibration weights</subject><subject>Regression</subject><subject>Regression analysis</subject><subject>regression weighting</subject><subject>Samples</subject><subject>Simulation</subject><subject>Trimming</subject><subject>Weight</subject><subject>Weighting methods</subject><issn>0306-7734</issn><issn>1751-5823</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><recordid>eNp9kM1Lw0AQxRdRsFYv3oWAFxFSZ783Rylai59YpeJlSdpNuzXN1t1U2__eaNSDB-cyh_d7w7yH0D6GDq7nxJbBdzCBRG6gFpYcx1wRuolaQEHEUlK2jXZCmAEAJYq1EO6mhc18WllXRkNjJ9PKlpPo2lRTNw5R7nzUdfNFYVbRYOnfzDrsoq08LYLZ-95t9Hh-9tC9iK9ue_3u6VU8ogmTMQHAjNBUjQ1J2JjkIGSSZoQRISBTGHMJQmVjTrFiUhJCQBqF81wBz0ZU0TY6au4uvHtdmlDpuQ0jUxRpadwyaKxA1UkZZTV6-AeduaUv6-80lopzACmTmjpuqJF3IXiT64W389SvNQb92Z7-bE9_tVfDuIHfbWHW_5C6fzO4__EcNJ5ZqJz_9TCGBWGC1Hrc6DZUZvWrp_5FC0kl18Obnr67ZFQ882v9RD8AULuHQQ</recordid><startdate>201604</startdate><enddate>201604</enddate><creator>Wu, Changbao</creator><creator>Lu, Wilson W.</creator><general>Blackwell Publishing Ltd</general><general>Blackwell Publishing</general><general>John Wiley & Sons, Inc</general><scope>BSCLL</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>H8D</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>201604</creationdate><title>Calibration Weighting Methods for Complex Surveys</title><author>Wu, Changbao ; Lu, Wilson W.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3947-2001423a8de294d2f0679ab242660b81157068bd531847722207e81ff805bc383</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Calibration</topic><topic>Computer simulation</topic><topic>Empirical analysis</topic><topic>Exponential tilting</topic><topic>Kullback-Leibler distance</topic><topic>Mathematical analysis</topic><topic>Newton-Raphson procedure</topic><topic>Polls & surveys</topic><topic>pseudo empirical likelihood</topic><topic>range-restricted calibration weights</topic><topic>Regression</topic><topic>Regression analysis</topic><topic>regression weighting</topic><topic>Samples</topic><topic>Simulation</topic><topic>Trimming</topic><topic>Weight</topic><topic>Weighting methods</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wu, Changbao</creatorcontrib><creatorcontrib>Lu, Wilson W.</creatorcontrib><collection>Istex</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>Aerospace Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>International statistical review</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wu, Changbao</au><au>Lu, Wilson W.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Calibration Weighting Methods for Complex Surveys</atitle><jtitle>International statistical review</jtitle><addtitle>International Statistical Review</addtitle><date>2016-04</date><risdate>2016</risdate><volume>84</volume><issue>1</issue><spage>79</spage><epage>98</epage><pages>79-98</pages><issn>0306-7734</issn><eissn>1751-5823</eissn><abstract>This paper presents a careful investigation of the three popular calibration weighting methods: (i) generalised regression; (ii) generalised exponential tilting and (iii) generalised pseudo empirical likelihood, with a major focus on computational aspects of the methods and some empirical evidences on calibrated weights. 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| ispartof | International statistical review, 2016-04, Vol.84 (1), p.79-98 |
| issn | 0306-7734 1751-5823 |
| language | eng |
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| source | Wiley Online Library Journals Frontfile Complete; JSTOR Mathematics & Statistics; Jstor Complete Legacy |
| subjects | Calibration Computer simulation Empirical analysis Exponential tilting Kullback-Leibler distance Mathematical analysis Newton-Raphson procedure Polls & surveys pseudo empirical likelihood range-restricted calibration weights Regression Regression analysis regression weighting Samples Simulation Trimming Weight Weighting methods |
| title | Calibration Weighting Methods for Complex Surveys |
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