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Article: The mean-shift outlier model in general weighted regression and its applications
Title | The mean-shift outlier model in general weighted regression and its applications |
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Authors | |
Keywords | Generalized Estimating Equations Influential Observations One-Step Approximation Regression Diagnostics |
Issue Date | 1999 |
Publisher | Elsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/csda |
Citation | Computational Statistics And Data Analysis, 1999, v. 30 n. 4, p. 429-441 How to Cite? |
Abstract | Consider the general weighted linear regression model y=Xβ+ε, where E(ε)=0,Cov(ε)=Vσ 2,σ 2 is an unknown positive scalar, and V is a symmetric positive-definite matrix not necessary diagonal. Two models, the mean-shift outlier model and the case-deletion model, can be employed to develop multiple case-deletion diagnostics for the linear model. The multiple case-deletion diagnostics are obtained via the mean-shift outlier model in this article and are shown to be equivalent to the deletion diagnostics via the case deletion model obtained by Preisser and Qaqish (1996, Biometrika, 83, 551-562). In addition, computing the multiple case-deletion diagnostics obtained via the mean-shift outlier model is faster than computing the one based on the more commonly used case-deletion model in some situations. Applications of the multiple deletion diagnostics developed from the mean-shift outlier model are also given for regression analysis with the likelihood function available and regression analysis based on generalized estimating equations. These applications include survival models and the generalized estimating equations of Liang and Zeger (1986, Biometrika, 73, 13-22). Several numerical experiments as well as a real example are given as illustrations. © 1999 Elsevier Science B.V. |
Persistent Identifier | http://hdl.handle.net/10722/172381 |
ISSN | 2023 Impact Factor: 1.5 2023 SCImago Journal Rankings: 1.008 |
ISI Accession Number ID | |
References |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Wei, WH | en_US |
dc.contributor.author | Fung, WK | en_US |
dc.date.accessioned | 2012-10-30T06:22:14Z | - |
dc.date.available | 2012-10-30T06:22:14Z | - |
dc.date.issued | 1999 | en_US |
dc.identifier.citation | Computational Statistics And Data Analysis, 1999, v. 30 n. 4, p. 429-441 | en_US |
dc.identifier.issn | 0167-9473 | en_US |
dc.identifier.uri | http://hdl.handle.net/10722/172381 | - |
dc.description.abstract | Consider the general weighted linear regression model y=Xβ+ε, where E(ε)=0,Cov(ε)=Vσ 2,σ 2 is an unknown positive scalar, and V is a symmetric positive-definite matrix not necessary diagonal. Two models, the mean-shift outlier model and the case-deletion model, can be employed to develop multiple case-deletion diagnostics for the linear model. The multiple case-deletion diagnostics are obtained via the mean-shift outlier model in this article and are shown to be equivalent to the deletion diagnostics via the case deletion model obtained by Preisser and Qaqish (1996, Biometrika, 83, 551-562). In addition, computing the multiple case-deletion diagnostics obtained via the mean-shift outlier model is faster than computing the one based on the more commonly used case-deletion model in some situations. Applications of the multiple deletion diagnostics developed from the mean-shift outlier model are also given for regression analysis with the likelihood function available and regression analysis based on generalized estimating equations. These applications include survival models and the generalized estimating equations of Liang and Zeger (1986, Biometrika, 73, 13-22). Several numerical experiments as well as a real example are given as illustrations. © 1999 Elsevier Science B.V. | en_US |
dc.language | eng | en_US |
dc.publisher | Elsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/csda | en_US |
dc.relation.ispartof | Computational Statistics and Data Analysis | en_US |
dc.subject | Generalized Estimating Equations | en_US |
dc.subject | Influential Observations | en_US |
dc.subject | One-Step Approximation | en_US |
dc.subject | Regression Diagnostics | en_US |
dc.title | The mean-shift outlier model in general weighted regression and its applications | en_US |
dc.type | Article | en_US |
dc.identifier.email | Fung, WK: wingfung@hku.hk | en_US |
dc.identifier.authority | Fung, WK=rp00696 | en_US |
dc.description.nature | link_to_subscribed_fulltext | en_US |
dc.identifier.scopus | eid_2-s2.0-0032634822 | en_US |
dc.relation.references | http://www.scopus.com/mlt/select.url?eid=2-s2.0-0032634822&selection=ref&src=s&origin=recordpage | en_US |
dc.identifier.volume | 30 | en_US |
dc.identifier.issue | 4 | en_US |
dc.identifier.spage | 429 | en_US |
dc.identifier.epage | 441 | en_US |
dc.identifier.isi | WOS:000080918600006 | - |
dc.publisher.place | Netherlands | en_US |
dc.identifier.scopusauthorid | Wei, WH=7403321505 | en_US |
dc.identifier.scopusauthorid | Fung, WK=13310399400 | en_US |
dc.identifier.issnl | 0167-9473 | - |