Hybrid Correlation Coefficient of Spearman with MM-Estimator

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dc.authorscopusid58520622900
dc.authorscopusid54411219800
dc.authorscopusid24481107300
dc.authorscopusid57189497995
dc.authorscopusid36558924800
dc.contributor.authorBakar, Siti Hajar Binti Abu
dc.contributor.authorLola, Muhamad Safiih Bin
dc.contributor.authorKamil, Anton Abdulbasah
dc.contributor.authorZainuddin, Nurul Hila
dc.contributor.authorAbdullah, Mohd Tajuddin
dc.date.accessioned2024-09-11T19:58:28Z
dc.date.available2024-09-11T19:58:28Z
dc.date.issued2023
dc.departmentİstanbul Gelişim Üniversitesien_US
dc.description.abstractThe Spearman rho nonparametric correlation coefficient is widely used to measure the strength and degree of association between two variables. However, outliers in the data can skew the results, leading to inaccurate results as the Spearman correlation coefficient is sensitive toward outliers. Thus, the robust approach is used to construct a robust model which is highly resistant to data contamination. The robustness of an estimator is measured by the breakdown point which is the smallest fraction of outliers in a sample data without affecting the estimator entirely. To overcome this problem, the aim of this study is two-fold. Firstly, researchers have proposed a robust Spearman correlation coefficient model based on the MMestimator, called the MM-Spearman correlation coefficient. Secondly, to test the performance of the proposed model, it was tested by the Monte Carlo simulation and contaminated air pollution data in Kuala Terengganu, Terengganu, Malaysia. The data have been contaminated from 10% to 50% outliers. The performance of the MMSpearman correlation coefficient properties was evaluated by statistical measurements such as standard error, mean squared error, root mean squared error and bias. The MMSpearman correlation coefficient model outperformed the classical model, producing significantly smaller standard error, mean squared error, and root mean squared error values. The robustness of the model was evaluated using the breakdown point, which measures the smallest fraction of outliers that can be present in sample data without entirely affecting the estimator. The hybrid MM-Spearman correlation coefficient model demonstrated high robustness and efficiently handled data contamination up to 50%. However, the study has a limitation in that it can only overcome data contamination up to a maximum of 50%. Despite this limitation, the proposed model provides accurate and efficient results, enabling management authorities to make sound decisions without being affected by contaminated data. The MM-Spearman correlation coefficient model provides a valuable tool for researchers and decision-makers, allowing them to analyze data with a high degree of accuracy and robustness, even in the presence of outliers. © 2023 by authors, all rights reserved.en_US
dc.identifier.doi10.13189/ms.2023.110411
dc.identifier.endpage702en_US
dc.identifier.issn2332-2071en_US
dc.identifier.issue4en_US
dc.identifier.scopus2-s2.0-85166512499en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage693en_US
dc.identifier.urihttps://doi.org/10.13189/ms.2023.110411
dc.identifier.urihttps://hdl.handle.net/11363/8492
dc.identifier.volume11en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherHorizon Research Publishingen_US
dc.relation.ispartofMathematics and Statisticsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz20240903_Gen_US
dc.subjectCoefficient Correlation Spearman; Hybrid Model; MM-estimator; Outliers; Robustnessen_US
dc.titleHybrid Correlation Coefficient of Spearman with MM-Estimatoren_US
dc.typeArticleen_US

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