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: | , |
|---|---|
| Format: | Article |
| Published: |
National University of Malaysia Press
2019
|
| _version_ | 1825951252421279744 |
|---|---|
| author | Midi, Habshah Ab. Aziz, Nasuhar |
| author_facet | Midi, Habshah Ab. Aziz, Nasuhar |
| author_sort | Midi, Habshah |
| collection | UPM |
| 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 | 2024-03-06T10:27:10Z |
| format | Article |
| id | upm.eprints-80061 |
| institution | Universiti Putra Malaysia |
| last_indexed | 2024-03-06T10:27:10Z |
| publishDate | 2019 |
| publisher | National University of Malaysia Press |
| record_format | dspace |
| spelling | upm.eprints-800612023-08-07T08:56:50Z http://psasir.upm.edu.my/id/eprint/80061/ High breakdown estimator for dual response optimization in the presence of outliers Midi, Habshah Ab. Aziz, Nasuhar 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. National University of Malaysia Press 2019 Article PeerReviewed Midi, Habshah and Ab. Aziz, Nasuhar (2019) High breakdown estimator for dual response optimization in the presence of outliers. Sains Malaysiana, 48 (8). pp. 1771-1776. ISSN 0126-6039 https://www.ukm.my/jsm/english_journals/vol48num8_2019/vol48num8_2019pg1771-1776.html |
| spellingShingle | Midi, Habshah Ab. Aziz, Nasuhar 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 |
| work_keys_str_mv | AT midihabshah highbreakdownestimatorfordualresponseoptimizationinthepresenceofoutliers AT abaziznasuhar highbreakdownestimatorfordualresponseoptimizationinthepresenceofoutliers |