| _version_ |
1860797189833359360
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| building |
INTELEK Repository
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| collection |
Online Access
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| collectionurl |
https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072
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| date |
2017-12-19 12:06:33
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| format |
Restricted Document
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| id |
11717
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| institution |
UniSZA
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| originalfilename |
5995-01-FH02-ICODE-18-13380.pdf
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| person |
Pusat Komputer
pusat komputer
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| recordtype |
oai_dc
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| resourceurl |
https://intelek.unisza.edu.my/intelek/pages/view.php?ref=11717
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| spelling |
11717 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=11717 https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072 Restricted Document Article Journal application/pdf 6 1.6 Adobe Acrobat Pro DC 20 Paper Capture Plug-in Pusat Komputer pusat komputer 2017-12-19 12:06:33 5995-01-FH02-ICODE-18-13380.pdf UniSZA Private Access Outlier detection based on robust parameter estimates International Journal of Applied Engineering Research Outliers can influence the analysis of data in various different ways. The outliers can lead to model misspecification, incorrect analysis results and can make all estimation procedures meaningless. In regression analysis, ordinary least square estimation is most frequently used for estimation of the parameters in the model. Unfortunately, this estimator is sensitive to outliers. Thus, in this paper we proposed some statistics for detection of outliers based on robust estimation, namely least trimmed squares (LTS). A simulation study was performed to prove that the alternative approach gives a better results than OLS estimation to identify outliers. 12 23 13429-13434
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| spellingShingle |
Outlier detection based on robust parameter estimates
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| summary |
Outliers can influence the analysis of data in various different ways. The outliers can lead to model misspecification, incorrect analysis results and can make all estimation procedures meaningless. In regression analysis, ordinary least square estimation is most frequently used for estimation of the parameters in the model. Unfortunately, this estimator is sensitive to outliers. Thus, in this paper we proposed some statistics for detection of outliers based on robust estimation, namely least trimmed squares (LTS). A simulation study was performed to prove that the alternative approach gives a better results than OLS estimation to identify outliers.
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| title |
Outlier detection based on robust parameter estimates
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| title_full |
Outlier detection based on robust parameter estimates
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| title_fullStr |
Outlier detection based on robust parameter estimates
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| title_full_unstemmed |
Outlier detection based on robust parameter estimates
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| title_short |
Outlier detection based on robust parameter estimates
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| title_sort |
outlier detection based on robust parameter estimates
|