Difference between revisions of "Hekimoğlu 2005 zfv"
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{{Publication | {{Publication | ||
|title=Hekimoğlu S (2005) Do robust methods identify outliers more reliably than conventional tests for outliers?. zfv 130(3):174-180. | |title=Hekimoğlu S (2005) Do robust methods identify outliers more reliably than conventional tests for outliers?. zfv 130(3):174-180. | ||
|info=[https://www.semanticscholar.org/paper/Do-Robust-Methods-Identify-Outliniers-More-Reliably-Hekimo%C4%9Flu/6082da238036c8509a0e991089ec4fa929ed862c | |info=[https://www.semanticscholar.org/paper/Do-Robust-Methods-Identify-Outliniers-More-Reliably-Hekimo%C4%9Flu/6082da238036c8509a0e991089ec4fa929ed862c Open Access] | ||
|authors=Hekimoğlu S | |authors=Hekimoğlu S | ||
|year=2005 | |year=2005 | ||
|journal= | |journal=zfv | ||
|abstract=In order to identify outliers, there are two approaches: the conventional tests for outliers and robust methods. Statisticians working with robust methods argue that their results are more reliable than the conventional tests for outliers. Which one of these approaches is more reliable? This question is investigated here in view of the problems caused by masking effects, swamping effects and leverage points and discussed by simulated linear regression models. The mean success rate is used to compare the two approaches. Summarizing, the robust methods can identify outliers at a rate of 22% more reliably than the conventional test for outliers in a simple regression. | |abstract=In order to identify outliers, there are two approaches: the conventional tests for outliers and robust methods. Statisticians working with robust methods argue that their results are more reliable than the conventional tests for outliers. Which one of these approaches is more reliable? This question is investigated here in view of the problems caused by masking effects, swamping effects and leverage points and discussed by simulated linear regression models. The mean success rate is used to compare the two approaches. Summarizing, the robust methods can identify outliers at a rate of 22% more reliably than the conventional test for outliers in a simple regression. | ||
|editor=[[Iglesias-Gonzalez J]] | |editor=[[Iglesias-Gonzalez J]] | ||
}} | }} | ||
== Cited by == | |||
{{Template:Cited by Iglesias-Gonzalez 2021 MitoFit PT}} | |||
{{Labeling | {{Labeling | ||
|additional=MitoFit 2021 PT | |additional=MitoFit 2021 PT | ||
}} | }} | ||
Latest revision as of 14:59, 26 January 2021
| Hekimoğlu S (2005) Do robust methods identify outliers more reliably than conventional tests for outliers?. zfv 130(3):174-180. |
Hekimoğlu S (2005) zfv
Abstract: In order to identify outliers, there are two approaches: the conventional tests for outliers and robust methods. Statisticians working with robust methods argue that their results are more reliable than the conventional tests for outliers. Which one of these approaches is more reliable? This question is investigated here in view of the problems caused by masking effects, swamping effects and leverage points and discussed by simulated linear regression models. The mean success rate is used to compare the two approaches. Summarizing, the robust methods can identify outliers at a rate of 22% more reliably than the conventional test for outliers in a simple regression.
• Bioblast editor: Iglesias-Gonzalez J
Cited by
- Iglesias-Gonzalez et al (2021) Proficiency test in mt-respiration: A necessary tool for reliable and reproducible results. MitoFit Preprints 2021 (in prep).
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