Robust Weighted Approaches to Detect and Deal with Outliers in Estimating Principal Component Regression Model
<br /><span>Abstract</span><br /><span>This paper aims to propose an approach to deal with the problem of Multi-Collinearity between the explanatory variables and outliers in the data by using the method of Principal Component Regression, and then using a robust weighti...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | Arabic |
| Published: |
College of Computer Science and Mathematics, University of Mosul
2021-06-01
|
| Series: | المجلة العراقية للعلوم الاحصائية |
| Subjects: | |
| Online Access: | https://stats.mosuljournals.com/article_168371_13376e8e9b407ec86d3cce6706e57b6d.pdf |
| _version_ | 1811242487715463168 |
|---|---|
| author | Esraa Alsaraf Bashar AL-Talib |
| author_facet | Esraa Alsaraf Bashar AL-Talib |
| author_sort | Esraa Alsaraf |
| collection | DOAJ |
| description | <br /><span>Abstract</span><br /><span>This paper aims to propose an approach to deal with the problem of Multi-Collinearity between the explanatory variables and outliers in the data by using the method of Principal Component Regression, and then using a robust weighting functions for the objective function has been used to deal with the presence of outliers in the data, and in order to verify the efficiency of the estimators, an experimental study was conducted through the simulation approach, and the methods were also applied to real data collected from the files of Badoush Cement Factory in Nineveh Governorate for the period from (2008-2014) with nine explanatory variables representing the chemical properties of cement and a dependent variable representing the physical properties of cement (hardness).</span><br /><span>The data was tested whether it was suffer from multi-collinearity problem and then the least squares using principal components as an explanatory variables and the model was estimated, and it was found that the variables suffer from Multi-Collinearity problem, and the treatment was done by applying principal component regression weighed by robust weights due to the presence of outlying values in the data in addition to the collinearity problem.</span> |
| first_indexed | 2024-04-12T13:52:57Z |
| format | Article |
| id | doaj.art-26075f7a354047399a3badece2b719ef |
| institution | Directory Open Access Journal |
| issn | 1680-855X 2664-2956 |
| language | Arabic |
| last_indexed | 2024-04-12T13:52:57Z |
| publishDate | 2021-06-01 |
| publisher | College of Computer Science and Mathematics, University of Mosul |
| record_format | Article |
| series | المجلة العراقية للعلوم الاحصائية |
| spelling | doaj.art-26075f7a354047399a3badece2b719ef2022-12-22T03:30:28ZaraCollege of Computer Science and Mathematics, University of Mosulالمجلة العراقية للعلوم الاحصائية1680-855X2664-29562021-06-0118112110.33899/iqjoss.2021.168371168371Robust Weighted Approaches to Detect and Deal with Outliers in Estimating Principal Component Regression ModelEsraa Alsaraf0Bashar AL-Talib1Department of Statistics and InformaticsDepartment of Statistics and Informatics, Faculty of Computer Sciences and Mathematics, University of Mosul, Mosul, Iraq<br /><span>Abstract</span><br /><span>This paper aims to propose an approach to deal with the problem of Multi-Collinearity between the explanatory variables and outliers in the data by using the method of Principal Component Regression, and then using a robust weighting functions for the objective function has been used to deal with the presence of outliers in the data, and in order to verify the efficiency of the estimators, an experimental study was conducted through the simulation approach, and the methods were also applied to real data collected from the files of Badoush Cement Factory in Nineveh Governorate for the period from (2008-2014) with nine explanatory variables representing the chemical properties of cement and a dependent variable representing the physical properties of cement (hardness).</span><br /><span>The data was tested whether it was suffer from multi-collinearity problem and then the least squares using principal components as an explanatory variables and the model was estimated, and it was found that the variables suffer from Multi-Collinearity problem, and the treatment was done by applying principal component regression weighed by robust weights due to the presence of outlying values in the data in addition to the collinearity problem.</span>https://stats.mosuljournals.com/article_168371_13376e8e9b407ec86d3cce6706e57b6d.pdfprincipal component regressionoutliersleverage pointsweighted least squaresmulti-collinearity |
| spellingShingle | Esraa Alsaraf Bashar AL-Talib Robust Weighted Approaches to Detect and Deal with Outliers in Estimating Principal Component Regression Model المجلة العراقية للعلوم الاحصائية principal component regression outliers leverage points weighted least squares multi-collinearity |
| title | Robust Weighted Approaches to Detect and Deal with Outliers in Estimating Principal Component Regression Model |
| title_full | Robust Weighted Approaches to Detect and Deal with Outliers in Estimating Principal Component Regression Model |
| title_fullStr | Robust Weighted Approaches to Detect and Deal with Outliers in Estimating Principal Component Regression Model |
| title_full_unstemmed | Robust Weighted Approaches to Detect and Deal with Outliers in Estimating Principal Component Regression Model |
| title_short | Robust Weighted Approaches to Detect and Deal with Outliers in Estimating Principal Component Regression Model |
| title_sort | robust weighted approaches to detect and deal with outliers in estimating principal component regression model |
| topic | principal component regression outliers leverage points weighted least squares multi-collinearity |
| url | https://stats.mosuljournals.com/article_168371_13376e8e9b407ec86d3cce6706e57b6d.pdf |
| work_keys_str_mv | AT esraaalsaraf robustweightedapproachestodetectanddealwithoutliersinestimatingprincipalcomponentregressionmodel AT basharaltalib robustweightedapproachestodetectanddealwithoutliersinestimatingprincipalcomponentregressionmodel |