High breakdown estimator for dual response optimization in the presence of outliers
Nowadays, dual response surface approach is used extensively, and it is known as one of the powerful tools for robust design. General assumptions are the data is normally distributed, and there is no outlier in the data set. The traditional procedures for robust design is to establish the process lo...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Penerbit Universiti Kebangsaan Malaysia
2019
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| Online Access: | http://journalarticle.ukm.my/13901/1/24%20Habshah%20Midi.pdf |
| _version_ | 1796931961330073600 |
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| author | Habshah Midi, Nasuhar Ab. Aziz, |
| author_facet | Habshah Midi, Nasuhar Ab. Aziz, |
| author_sort | Habshah Midi, |
| collection | UKM |
| description | Nowadays, dual response surface approach is used extensively, and it is known as one of the powerful tools for robust design. General assumptions are the data is normally distributed, and there is no outlier in the data set. The traditional procedures for robust design is to establish the process location and process scale models of the response variable based on sample mean and sample variance, respectively. Meanwhile, the ordinary least squares (OLS) method is often used to estimate the parameters of the regression response location and scale models. Nevertheless, many statistics practitioners are unaware that these existing procedures are easily influenced by outliers, and hence resulted in less accurate estimated mean response obtained through non-resistant method. As an alternative, the use of MM-location, MM-scale estimator, and MM regression estimator is proposed, in order to overcome the shortcomings of the existing procedures. This study employs a new penalty function optimization scheme to determine the optimum factor settings for robust design variables. The effectiveness of our proposed methods is confirmed by well-known example and Monte Carlo simulations. |
| first_indexed | 2024-03-06T04:25:51Z |
| format | Article |
| id | ukm.eprints-13901 |
| institution | Universiti Kebangsaan Malaysia |
| language | English |
| last_indexed | 2024-03-06T04:25:51Z |
| publishDate | 2019 |
| publisher | Penerbit Universiti Kebangsaan Malaysia |
| record_format | dspace |
| spelling | ukm.eprints-139012020-01-10T08:53:41Z http://journalarticle.ukm.my/13901/ High breakdown estimator for dual response optimization in the presence of outliers Habshah Midi, Nasuhar Ab. Aziz, Nowadays, dual response surface approach is used extensively, and it is known as one of the powerful tools for robust design. General assumptions are the data is normally distributed, and there is no outlier in the data set. The traditional procedures for robust design is to establish the process location and process scale models of the response variable based on sample mean and sample variance, respectively. Meanwhile, the ordinary least squares (OLS) method is often used to estimate the parameters of the regression response location and scale models. Nevertheless, many statistics practitioners are unaware that these existing procedures are easily influenced by outliers, and hence resulted in less accurate estimated mean response obtained through non-resistant method. As an alternative, the use of MM-location, MM-scale estimator, and MM regression estimator is proposed, in order to overcome the shortcomings of the existing procedures. This study employs a new penalty function optimization scheme to determine the optimum factor settings for robust design variables. The effectiveness of our proposed methods is confirmed by well-known example and Monte Carlo simulations. Penerbit Universiti Kebangsaan Malaysia 2019-08 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/13901/1/24%20Habshah%20Midi.pdf Habshah Midi, and Nasuhar Ab. Aziz, (2019) High breakdown estimator for dual response optimization in the presence of outliers. Sains Malaysiana, 48 (8). pp. 1771-1776. ISSN 0126-6039 http://www.ukm.my/jsm/malay_journals/jilid48bil8_2019/KandunganJilid48Bil8_2019.html |
| spellingShingle | Habshah Midi, Nasuhar Ab. Aziz, High breakdown estimator for dual response optimization in the presence of outliers |
| title | High breakdown estimator for dual response optimization
in the presence of outliers |
| title_full | High breakdown estimator for dual response optimization
in the presence of outliers |
| title_fullStr | High breakdown estimator for dual response optimization
in the presence of outliers |
| title_full_unstemmed | High breakdown estimator for dual response optimization
in the presence of outliers |
| title_short | High breakdown estimator for dual response optimization
in the presence of outliers |
| title_sort | high breakdown estimator for dual response optimization in the presence of outliers |
| url | http://journalarticle.ukm.my/13901/1/24%20Habshah%20Midi.pdf |
| work_keys_str_mv | AT habshahmidi highbreakdownestimatorfordualresponseoptimizationinthepresenceofoutliers AT nasuharabaziz highbreakdownestimatorfordualresponseoptimizationinthepresenceofoutliers |