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Comparison of GEE, MINQUE, ML, and REML Estimating Equations for Normally Distributed Data
Generalized estimating equations (GEE) provide a regression framework for analyzing correlated data that are not necessarily assumed to be normal. For linear mixed models assuming normality, maximum likelihood (ML) and restricted maximum likelihood (REML) are commonly used for estimating variance an...
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| Published in: | The American statistician 2001-05, Vol.55 (2), p.125-130 |
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| Main Authors: | , , |
| Format: | Article |
| Language: | English |
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| cited_by | cdi_FETCH-LOGICAL-c375t-d9ec5a292a82657fdfe18a34d39f3038bac1bb8c6e6b6e4ed2e4d8e23f3cfe213 |
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| cites | cdi_FETCH-LOGICAL-c375t-d9ec5a292a82657fdfe18a34d39f3038bac1bb8c6e6b6e4ed2e4d8e23f3cfe213 |
| container_end_page | 130 |
| container_issue | 2 |
| container_start_page | 125 |
| container_title | The American statistician |
| container_volume | 55 |
| creator | Wu, Chi-tsung Gumpertz, Marcia L Boos, Dennis D |
| description | Generalized estimating equations (GEE) provide a regression framework for analyzing correlated data that are not necessarily assumed to be normal. For linear mixed models assuming normality, maximum likelihood (ML) and restricted maximum likelihood (REML) are commonly used for estimating variance and covariance parameters. In the analysis of variance tradition, minimum norm quadratic unbiased estimation (MINQUE) has been developed to estimate variance and covariance components without relying on distributional assumptions. This article rewrites the ML, REML, and MINQUE estimating equations in a form similar to GEE. This form is not particularly useful for computations, but it provides a very clear picture of the similarities and differences of the four methods. The derivations are straightforward and suitable for a linear models course. |
| doi_str_mv | 10.1198/000313001750358608 |
| format | article |
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The derivations are straightforward and suitable for a linear models course.</description><identifier>ISSN: 0003-1305</identifier><identifier>EISSN: 1537-2731</identifier><identifier>DOI: 10.1198/000313001750358608</identifier><identifier>CODEN: ASTAAJ</identifier><language>eng</language><publisher>Alexandria, VA: Taylor & Francis</publisher><subject>Analytical estimating ; Biometrics ; Correlated data ; Covariance ; Covariance matrices ; Data analysis ; Estimated generalized least squares ; Estimation methods ; Estimators for the mean ; Exact sciences and technology ; Generalized estimating equations ; Generalized linear models ; Linear inference, regression ; Linear models ; Marginal model ; Mathematical models ; Mathematics ; Maximum likelihood estimation ; Multivariate analysis ; Parameter estimation ; Parametric models ; Probability and statistics ; Sciences and techniques of general use ; Statistical analysis ; Statistical variance ; Statistics ; Unbiased estimators ; Variance-covariance parameter estimation</subject><ispartof>The American statistician, 2001-05, Vol.55 (2), p.125-130</ispartof><rights>American Statistical Association 2001</rights><rights>Copyright 2001 American Statistical Association</rights><rights>2001 INIST-CNRS</rights><rights>Copyright American Statistical Association May 2001</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c375t-d9ec5a292a82657fdfe18a34d39f3038bac1bb8c6e6b6e4ed2e4d8e23f3cfe213</citedby><cites>FETCH-LOGICAL-c375t-d9ec5a292a82657fdfe18a34d39f3038bac1bb8c6e6b6e4ed2e4d8e23f3cfe213</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/228500294/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$H</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/228500294?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,780,784,11688,21394,27924,27925,33611,36060,43733,44363,58238,58471,74221,74895</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=987513$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Wu, Chi-tsung</creatorcontrib><creatorcontrib>Gumpertz, Marcia L</creatorcontrib><creatorcontrib>Boos, Dennis D</creatorcontrib><title>Comparison of GEE, MINQUE, ML, and REML Estimating Equations for Normally Distributed Data</title><title>The American statistician</title><description>Generalized estimating equations (GEE) provide a regression framework for analyzing correlated data that are not necessarily assumed to be normal. For linear mixed models assuming normality, maximum likelihood (ML) and restricted maximum likelihood (REML) are commonly used for estimating variance and covariance parameters. In the analysis of variance tradition, minimum norm quadratic unbiased estimation (MINQUE) has been developed to estimate variance and covariance components without relying on distributional assumptions. This article rewrites the ML, REML, and MINQUE estimating equations in a form similar to GEE. This form is not particularly useful for computations, but it provides a very clear picture of the similarities and differences of the four methods. 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| ispartof | The American statistician, 2001-05, Vol.55 (2), p.125-130 |
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| source | JSTOR Archival Journals and Primary Sources Collection; Social Science Premium Collection; ABI/INFORM Global; Taylor and Francis:Jisc Collections:Taylor and Francis Read and Publish Agreement 2024-2025:Science and Technology Collection (Reading list) |
| subjects | Analytical estimating Biometrics Correlated data Covariance Covariance matrices Data analysis Estimated generalized least squares Estimation methods Estimators for the mean Exact sciences and technology Generalized estimating equations Generalized linear models Linear inference, regression Linear models Marginal model Mathematical models Mathematics Maximum likelihood estimation Multivariate analysis Parameter estimation Parametric models Probability and statistics Sciences and techniques of general use Statistical analysis Statistical variance Statistics Unbiased estimators Variance-covariance parameter estimation |
| title | Comparison of GEE, MINQUE, ML, and REML Estimating Equations for Normally Distributed Data |
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