The performance of robust correlation coefficient under contaminated bivariate data
Classical correlation coefficient is a powerful statistical analysis when measuring a relationship between the bivariate normal distribution when the assumptions are fulfill.However, this classical correlation coefficient performs poor in the presence of outlier. Thus, this study aims to propose new...
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2016
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author | Zakaria, Nur Amira Abdullah, Suhaida Ahad, Nor Aishah Yusof, Norhayati Syed Yahaya, Sharipah Soaad |
author_facet | Zakaria, Nur Amira Abdullah, Suhaida Ahad, Nor Aishah Yusof, Norhayati Syed Yahaya, Sharipah Soaad |
author_sort | Zakaria, Nur Amira |
collection | UUM |
description | Classical correlation coefficient is a powerful statistical analysis when measuring a relationship between the bivariate normal distribution when the assumptions are fulfill.However, this classical correlation coefficient performs poor in the presence of outlier. Thus, this study aims to propose new version of robust correlation coefficient based on MAD n and S n .The performance of this proposed robust correlation coefficient will be evaluated based on three indicators which were the value of the correlation coefficient, average bias and standard error. The proposed procedure is expected to produce MAD n correlation coefficient and S n correlation coefficient.Both coefficients are expected to perform better than classical correlation coefficient and resistance to the outlier. |
first_indexed | 2024-07-04T06:12:39Z |
format | Conference or Workshop Item |
id | uum-20179 |
institution | Universiti Utara Malaysia |
last_indexed | 2024-07-04T06:12:39Z |
publishDate | 2016 |
record_format | eprints |
spelling | uum-201792016-12-04T06:59:07Z https://repo.uum.edu.my/id/eprint/20179/ The performance of robust correlation coefficient under contaminated bivariate data Zakaria, Nur Amira Abdullah, Suhaida Ahad, Nor Aishah Yusof, Norhayati Syed Yahaya, Sharipah Soaad QA75 Electronic computers. Computer science Classical correlation coefficient is a powerful statistical analysis when measuring a relationship between the bivariate normal distribution when the assumptions are fulfill.However, this classical correlation coefficient performs poor in the presence of outlier. Thus, this study aims to propose new version of robust correlation coefficient based on MAD n and S n .The performance of this proposed robust correlation coefficient will be evaluated based on three indicators which were the value of the correlation coefficient, average bias and standard error. The proposed procedure is expected to produce MAD n correlation coefficient and S n correlation coefficient.Both coefficients are expected to perform better than classical correlation coefficient and resistance to the outlier. 2016 Conference or Workshop Item PeerReviewed Zakaria, Nur Amira and Abdullah, Suhaida and Ahad, Nor Aishah and Yusof, Norhayati and Syed Yahaya, Sharipah Soaad (2016) The performance of robust correlation coefficient under contaminated bivariate data. In: 4th International Conference on Quantitative Sciences and Its Applications (ICOQSIA 2016), 16–18 August 2016, Bangi, Selangor, Malaysia. http://doi.org/10.1063/1.4966107 doi:10.1063/1.4966107 doi:10.1063/1.4966107 |
spellingShingle | QA75 Electronic computers. Computer science Zakaria, Nur Amira Abdullah, Suhaida Ahad, Nor Aishah Yusof, Norhayati Syed Yahaya, Sharipah Soaad The performance of robust correlation coefficient under contaminated bivariate data |
title | The performance of robust correlation coefficient under contaminated bivariate data |
title_full | The performance of robust correlation coefficient under contaminated bivariate data |
title_fullStr | The performance of robust correlation coefficient under contaminated bivariate data |
title_full_unstemmed | The performance of robust correlation coefficient under contaminated bivariate data |
title_short | The performance of robust correlation coefficient under contaminated bivariate data |
title_sort | performance of robust correlation coefficient under contaminated bivariate data |
topic | QA75 Electronic computers. Computer science |
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