Robust tests for one or more allometric lines.
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| Title: | Robust tests for one or more allometric lines. |
|---|---|
| Authors: | Taskinen, S.1,2 sara.l.taskinen@jyu.fi, Warton, D.I.1 david.warton@unsw.edu.au |
| Source: | Journal of Theoretical Biology. Sep2013, Vol. 333, p38-46. 9p. |
| Subjects: | Allometry, Regression analysis, Mathematical statistics, Maximum likelihood statistics, Error analysis in mathematics, Simulation methods & models, Parameter estimation |
| Abstract: | Abstract: In allometry, the study of how size variables scale against each other, it is often of interest to fit lines to bivariate data and test hypotheses about slope and elevation about one or several lines. The nature of the problem suggests that bivariate techniques related to principal component analysis are more appropriate than linear regression. Inference methods have been developed for this problem and are in widespread use, however, we demonstrate that such methods are not robust to bivariate contamination, and propose alternative approaches which are. The new approaches use Huber's M-estimator via a plug-in approach, where robust test procedures have the same form as classical ones, but where we plug in robust estimators of parameters and standard errors in place of classical estimators. Simulations demonstrate that these new procedures are robust against bivariate contamination, and can make accurate inferences even from small samples. [Copyright &y& Elsevier] |
| Copyright of Journal of Theoretical Biology is the property of Academic Press Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 89432718 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Robust tests for one or more allometric lines. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Taskinen%2C+S%2E%22">Taskinen, S.</searchLink><relatesTo>1,2</relatesTo><i> sara.l.taskinen@jyu.fi</i><br /><searchLink fieldCode="AR" term="%22Warton%2C+D%2EI%2E%22">Warton, D.I.</searchLink><relatesTo>1</relatesTo><i> david.warton@unsw.edu.au</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Theoretical+Biology%22">Journal of Theoretical Biology</searchLink>. Sep2013, Vol. 333, p38-46. 9p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Allometry%22">Allometry</searchLink><br /><searchLink fieldCode="DE" term="%22Regression+analysis%22">Regression analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+statistics%22">Mathematical statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Maximum+likelihood+statistics%22">Maximum likelihood statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Error+analysis+in+mathematics%22">Error analysis in mathematics</searchLink><br /><searchLink fieldCode="DE" term="%22Simulation+methods+%26+models%22">Simulation methods & models</searchLink><br /><searchLink fieldCode="DE" term="%22Parameter+estimation%22">Parameter estimation</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Abstract: In allometry, the study of how size variables scale against each other, it is often of interest to fit lines to bivariate data and test hypotheses about slope and elevation about one or several lines. The nature of the problem suggests that bivariate techniques related to principal component analysis are more appropriate than linear regression. Inference methods have been developed for this problem and are in widespread use, however, we demonstrate that such methods are not robust to bivariate contamination, and propose alternative approaches which are. The new approaches use Huber's M-estimator via a plug-in approach, where robust test procedures have the same form as classical ones, but where we plug in robust estimators of parameters and standard errors in place of classical estimators. Simulations demonstrate that these new procedures are robust against bivariate contamination, and can make accurate inferences even from small samples. [Copyright &y& Elsevier] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Theoretical Biology is the property of Academic Press Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.jtbi.2013.05.010 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 9 StartPage: 38 Subjects: – SubjectFull: Allometry Type: general – SubjectFull: Regression analysis Type: general – SubjectFull: Mathematical statistics Type: general – SubjectFull: Maximum likelihood statistics Type: general – SubjectFull: Error analysis in mathematics Type: general – SubjectFull: Simulation methods & models Type: general – SubjectFull: Parameter estimation Type: general Titles: – TitleFull: Robust tests for one or more allometric lines. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Taskinen, S. – PersonEntity: Name: NameFull: Warton, D.I. IsPartOfRelationships: – BibEntity: Dates: – D: 21 M: 09 Text: Sep2013 Type: published Y: 2013 Identifiers: – Type: issn-print Value: 00225193 Numbering: – Type: volume Value: 333 Titles: – TitleFull: Journal of Theoretical Biology Type: main |
| ResultId | 1 |
