Comparing groups using robust H statistic with adaptive trimmed mean
An alternative robust method for testing the equality of central tendency measures was developed by integrating H Statistic with adaptive trimmed mean using hinge estimator, HQ. H Statistic is known for its ability to control Type I error rates and HQ is a robust location estimator. This robust esti...
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Universiti Kebangsaan Malaysia
2014
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my-ukm.journal.70612016-12-14T06:43:00Z http://journalarticle.ukm.my/7061/ Comparing groups using robust H statistic with adaptive trimmed mean Nur Faraidah Muhammad Di, Sharipah Soaad Syed Yahaya, Suhaida Abdullah, An alternative robust method for testing the equality of central tendency measures was developed by integrating H Statistic with adaptive trimmed mean using hinge estimator, HQ. H Statistic is known for its ability to control Type I error rates and HQ is a robust location estimator. This robust estimator used asymmetric trimming technique, where it trims the tail of the distribution based on the characteristic of that particular distribution. To investigate on the performance (i.e. robustness) of the procedure, some variables were manipulated to create conditions which are known to highlight its strengths and weaknesses. Bootstrap method was used to test the hypothesis. The integration seemed to produce promising robust procedure that is capable of addressing the problem of violations to the assumptions. About 20% trimming is the appropriate amount of trimming for the procedure, where this amount is found to be robust in most conditions. This procedure was also proven to be robust as compared to the parametric (ANOVA) and non-parametric (Kruskal-Wallis) methods. Universiti Kebangsaan Malaysia 2014-04 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/7061/1/20_Nur_Faraidah.pdf Nur Faraidah Muhammad Di, and Sharipah Soaad Syed Yahaya, and Suhaida Abdullah, (2014) Comparing groups using robust H statistic with adaptive trimmed mean. Sains Malaysiana, 43 (4). pp. 643-648. ISSN 0126-6039 http://www.ukm.my/jsm/ |
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An alternative robust method for testing the equality of central tendency measures was developed by integrating H Statistic with adaptive trimmed mean using hinge estimator, HQ. H Statistic is known for its ability to control Type I error rates and HQ is a robust location estimator. This robust estimator used asymmetric trimming technique, where it trims the tail of the distribution based on the characteristic of that particular distribution. To investigate on the performance (i.e. robustness) of the procedure, some variables were manipulated to create conditions which are known to highlight its strengths and weaknesses. Bootstrap method was used to test the hypothesis. The integration seemed to produce promising robust procedure that is capable of addressing the problem of violations to the assumptions. About 20% trimming is the appropriate amount of trimming for the procedure, where this amount is found to be robust in most conditions. This procedure was also proven to be robust as compared to the parametric (ANOVA) and non-parametric (Kruskal-Wallis) methods. |
| format |
Article |
| author |
Nur Faraidah Muhammad Di, Sharipah Soaad Syed Yahaya, Suhaida Abdullah, |
| spellingShingle |
Nur Faraidah Muhammad Di, Sharipah Soaad Syed Yahaya, Suhaida Abdullah, Comparing groups using robust H statistic with adaptive trimmed mean |
| author_facet |
Nur Faraidah Muhammad Di, Sharipah Soaad Syed Yahaya, Suhaida Abdullah, |
| author_sort |
Nur Faraidah Muhammad Di, |
| title |
Comparing groups using robust H statistic with adaptive trimmed mean |
| title_short |
Comparing groups using robust H statistic with adaptive trimmed mean |
| title_full |
Comparing groups using robust H statistic with adaptive trimmed mean |
| title_fullStr |
Comparing groups using robust H statistic with adaptive trimmed mean |
| title_full_unstemmed |
Comparing groups using robust H statistic with adaptive trimmed mean |
| title_sort |
comparing groups using robust h statistic with adaptive trimmed mean |
| publisher |
Universiti Kebangsaan Malaysia |
| publishDate |
2014 |
| url |
http://journalarticle.ukm.my/7061/1/20_Nur_Faraidah.pdf http://journalarticle.ukm.my/7061/ http://www.ukm.my/jsm/ |
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