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A feasibility study in adapting Shamos Bickel and Hodges Lehman estimator into T-Method for normalization

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dc.citedby2
dc.contributor.authorHarudin N.en_US
dc.contributor.authorJamaludin K.R.en_US
dc.contributor.authorNabil Muhtazaruddin M.en_US
dc.contributor.authorRamlie F.en_US
dc.contributor.authorMuhamad W.Z.A.W.en_US
dc.contributor.authorid56319654100en_US
dc.contributor.authorid26434395500en_US
dc.contributor.authorid55578437800en_US
dc.contributor.authorid55982859700en_US
dc.contributor.authorid55860800560en_US
dc.date.accessioned2023-05-29T06:52:34Z
dc.date.available2023-05-29T06:52:34Z
dc.date.issued2018
dc.descriptionErrors; Forecasting; Manufacture; Parameter estimation; Classical methods; Classical statistics; Error percentage; Feasibility studies; Mahalanobis-taguchi systems; Mean and standard deviations; Multivariate data; Robust parameters; Population statisticsen_US
dc.description.abstractT-Method is one of the techniques governed under Mahalanobis Taguchi System that developed specifically for multivariate data predictions. Prediction using T-Method is always possible even with very limited sample size. The user of T-Method required to clearly understanding the population data trend since this method is not considering the effect of outliers within it. Outliers may cause apparent non-normality and the entire classical methods breakdown. There exist robust parameter estimate that provide satisfactory results when the data contain outliers, as well as when the data are free of them. The robust parameter estimates of location and scale measure called Shamos Bickel (SB) and Hodges Lehman (HL) which are used as a comparable method to calculate the mean and standard deviation of classical statistic is part of it. Embedding these into T-Method normalize stage feasibly help in enhancing the accuracy of the T-Method as well as analysing the robustness of T-method itself. However, the result of higher sample size case study shows that T-method is having lowest average error percentages (3.09%) on data with extreme outliers. HL and SB is having lowest error percentages (4.67%) for data without extreme outliers with minimum error differences compared to T-Method. The error percentages prediction trend is vice versa for lower sample size case study. The result shows that with minimum sample size, which outliers always be at low risk, T-Method is much better on that, while higher sample size with extreme outliers, T-Method as well show better prediction compared to others. For the case studies conducted in this research, it shows that normalization of T-Method is showing satisfactory results and it is not feasible to adapt HL and SB or normal mean and standard deviation into it since it's only provide minimum effect of percentages errors. Normalization using T-method is still considered having lower risk towards outlier's effect. � Published under licence by IOP Publishing Ltd.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo12033
dc.identifier.doi10.1088/1757-899X/319/1/012033
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85045625413
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85045625413&doi=10.1088%2f1757-899X%2f319%2f1%2f012033&partnerID=40&md5=fa739ab629842687f0a27c39ce5574c5
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/23867
dc.identifier.volume319
dc.publisherInstitute of Physics Publishingen_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleIOP Conference Series: Materials Science and Engineering
dc.titleA feasibility study in adapting Shamos Bickel and Hodges Lehman estimator into T-Method for normalizationen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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