Academic Journal
Assessing the regression to the mean for non-normal populations via kernel estimators.
| Τίτλος: | Assessing the regression to the mean for non-normal populations via kernel estimators. |
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| Συγγραφείς: | John, Majnu, Jawad, Abbas F. |
| Πηγή: | North American Journal of Medical Sciences; Jul2010, Vol. 2 Issue 7, p288-292, 5p, 1 Graph |
| Θεματικοί όροι: | Cystic fibrosis, Lung diseases, Regression analysis, Longitudinal method, Kernel functions, Statistical bootstrapping, Analysis of means, SAS (Computer program language), Empirical research, Patients |
| Περίληψη: | Background: Part of the change over time of a response in longitudinal studies may be attributed to the regression to the mean. The component of change due to regression to the mean is more pronounced in the subjects with extreme initial values. Das and Mulder proposed a nonparametric approach to estimate the regression to the mean. Aim: In this paper, Das and Mulder's method is made data-adaptive for empirical distributions via kernel estimation approaches, while retaining the original assumptions made by them. Results: We use the best approaches for kernel density and hazard function estimation in our methods. This makes our approach extremely user friendly for a practitioner via the state of the art procedures and packages available in statistical softwares such as SAS and R for kernel density and hazard function estimation. We also estimate the standard error of our estimates of regression to the mean via nonparametric bootstrap methods. Finally, our methods are illustrated by analyzing the percent predicted FEV1 measurements available from the Cystic Fibrosis Foundation's National Patient Registry. Conclusion: The kernel based approach presented in this article is a user-friendly method to assess the regression to the mean in non-normal populations. [ABSTRACT FROM AUTHOR] |
| Copyright of North American Journal of Medical Sciences is the property of North American Journal of Medical Sciences 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.) | |
| Βάση Δεδομένων: | Biomedical Index |
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| Header | DbId: edm DbLabel: Biomedical Index An: 60948439 RelevancyScore: 834 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 833.7705078125 |
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| Items | – Name: Title Label: Title Group: Ti Data: Assessing the regression to the mean for non-normal populations via kernel estimators. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22John%2C+Majnu%22">John, Majnu</searchLink><br /><searchLink fieldCode="AR" term="%22Jawad%2C+Abbas+F%2E%22">Jawad, Abbas F.</searchLink> – Name: TitleSource Label: Source Group: Src Data: North American Journal of Medical Sciences; Jul2010, Vol. 2 Issue 7, p288-292, 5p, 1 Graph – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Cystic+fibrosis%22">Cystic fibrosis</searchLink><br /><searchLink fieldCode="DE" term="%22Lung+diseases%22">Lung diseases</searchLink><br /><searchLink fieldCode="DE" term="%22Regression+analysis%22">Regression analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Longitudinal+method%22">Longitudinal method</searchLink><br /><searchLink fieldCode="DE" term="%22Kernel+functions%22">Kernel functions</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+bootstrapping%22">Statistical bootstrapping</searchLink><br /><searchLink fieldCode="DE" term="%22Analysis+of+means%22">Analysis of means</searchLink><br /><searchLink fieldCode="DE" term="%22SAS+%28Computer+program+language%29%22">SAS (Computer program language)</searchLink><br /><searchLink fieldCode="DE" term="%22Empirical+research%22">Empirical research</searchLink><br /><searchLink fieldCode="DE" term="%22Patients%22">Patients</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Background: Part of the change over time of a response in longitudinal studies may be attributed to the regression to the mean. The component of change due to regression to the mean is more pronounced in the subjects with extreme initial values. Das and Mulder proposed a nonparametric approach to estimate the regression to the mean. Aim: In this paper, Das and Mulder's method is made data-adaptive for empirical distributions via kernel estimation approaches, while retaining the original assumptions made by them. Results: We use the best approaches for kernel density and hazard function estimation in our methods. This makes our approach extremely user friendly for a practitioner via the state of the art procedures and packages available in statistical softwares such as SAS and R for kernel density and hazard function estimation. We also estimate the standard error of our estimates of regression to the mean via nonparametric bootstrap methods. Finally, our methods are illustrated by analyzing the percent predicted FEV1 measurements available from the Cystic Fibrosis Foundation's National Patient Registry. Conclusion: The kernel based approach presented in this article is a user-friendly method to assess the regression to the mean in non-normal populations. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of North American Journal of Medical Sciences is the property of North American Journal of Medical Sciences 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: Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 5 StartPage: 288 Subjects: – SubjectFull: Cystic fibrosis Type: general – SubjectFull: Lung diseases Type: general – SubjectFull: Regression analysis Type: general – SubjectFull: Longitudinal method Type: general – SubjectFull: Kernel functions Type: general – SubjectFull: Statistical bootstrapping Type: general – SubjectFull: Analysis of means Type: general – SubjectFull: SAS (Computer program language) Type: general – SubjectFull: Empirical research Type: general – SubjectFull: Patients Type: general Titles: – TitleFull: Assessing the regression to the mean for non-normal populations via kernel estimators. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: John, Majnu – PersonEntity: Name: NameFull: Jawad, Abbas F. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2010 Type: published Y: 2010 Identifiers: – Type: issn-print Value: 22501541 Numbering: – Type: volume Value: 2 – Type: issue Value: 7 Titles: – TitleFull: North American Journal of Medical Sciences Type: main |
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