Comparison of Robustness Non-Linearity Test in Computational Statistics when Outlier Detected.
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| Title: | Comparison of Robustness Non-Linearity Test in Computational Statistics when Outlier Detected. |
|---|---|
| Authors: | Rantini, Dwi1 dwi.rantini@ftmm.unair.ac.id, Ramadan, Arip2 aripramadan@telkomuniversity.ac.id, Sesay, Alhassan3 alhassansesay3@gmail.com |
| Source: | Engineering Letters. Dec2024, Vol. 32 Issue 12, p2299-2323. 25p. |
| Subjects: | Computational statistics, Regression analysis, Statistics |
| Abstract: | In regression modeling, we often encounter data with different ranges. Such data usually have outliers. If an outlier has a value far from the mean, it can cause an error in modeling. For example, data has a quadratic pattern, but because there are outliers, it can be indicated that the data is linear. This research will prove which non-linearity test is more robust if outlier data is shown. To prove this, data is generated, and outliers are found in the variable response of the nonlinear model. Using the RESET, the Terasvirta and White tests will prove to be more robust. The results show that the Terasvirta test is more robust than the RESET and White tests. This statement applies to models that are non-linear in parameters and variables. Therefore, if we want to test the goodness of a non-linear model and outliers are detected from our research, we recommend using the Terasvirta test. We prove that 53.42% of Terasvirta performs better than the RESET and White tests. Because the Terasvirta test is proven to be more robust if outliers are found in the non-linear model, this is very important to increase knowledge in education, especially in computing statistics. [ABSTRACT FROM AUTHOR] |
| Copyright of Engineering Letters is the property of International Association of Engineers (IAENG) 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 |
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| Items | – Name: Title Label: Title Group: Ti Data: Comparison of Robustness Non-Linearity Test in Computational Statistics when Outlier Detected. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Rantini%2C+Dwi%22">Rantini, Dwi</searchLink><relatesTo>1</relatesTo><i> dwi.rantini@ftmm.unair.ac.id</i><br /><searchLink fieldCode="AR" term="%22Ramadan%2C+Arip%22">Ramadan, Arip</searchLink><relatesTo>2</relatesTo><i> aripramadan@telkomuniversity.ac.id</i><br /><searchLink fieldCode="AR" term="%22Sesay%2C+Alhassan%22">Sesay, Alhassan</searchLink><relatesTo>3</relatesTo><i> alhassansesay3@gmail.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Engineering+Letters%22">Engineering Letters</searchLink>. Dec2024, Vol. 32 Issue 12, p2299-2323. 25p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Computational+statistics%22">Computational statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Regression+analysis%22">Regression analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Statistics%22">Statistics</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In regression modeling, we often encounter data with different ranges. Such data usually have outliers. If an outlier has a value far from the mean, it can cause an error in modeling. For example, data has a quadratic pattern, but because there are outliers, it can be indicated that the data is linear. This research will prove which non-linearity test is more robust if outlier data is shown. To prove this, data is generated, and outliers are found in the variable response of the nonlinear model. Using the RESET, the Terasvirta and White tests will prove to be more robust. The results show that the Terasvirta test is more robust than the RESET and White tests. This statement applies to models that are non-linear in parameters and variables. Therefore, if we want to test the goodness of a non-linear model and outliers are detected from our research, we recommend using the Terasvirta test. We prove that 53.42% of Terasvirta performs better than the RESET and White tests. Because the Terasvirta test is proven to be more robust if outliers are found in the non-linear model, this is very important to increase knowledge in education, especially in computing statistics. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Engineering Letters is the property of International Association of Engineers (IAENG) 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: 25 StartPage: 2299 Subjects: – SubjectFull: Computational statistics Type: general – SubjectFull: Regression analysis Type: general – SubjectFull: Statistics Type: general Titles: – TitleFull: Comparison of Robustness Non-Linearity Test in Computational Statistics when Outlier Detected. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Rantini, Dwi – PersonEntity: Name: NameFull: Ramadan, Arip – PersonEntity: Name: NameFull: Sesay, Alhassan IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 1816093X Numbering: – Type: volume Value: 32 – Type: issue Value: 12 Titles: – TitleFull: Engineering Letters Type: main |
| ResultId | 1 |
