Estimation parameters using Bisquare weighted robust ridge regression BRLTS estimator in the presence of multicollinearity and outliers
This study presents an improvement to robust ridge regression estimator. We proposed two methods Bisquare ridge least trimmed squares (BRLTS) and Bisquare ridge least absolute value (BRLAV) based on ridge least trimmed squares (RLTS) and ridge least absolute value (RLAV), respectively. We compared t...
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| creator | Pati, Kafi Dano Adnan, Robiah Rasheed, Bello Abdulkadir MD. J., Muhammad Alias |
| description | This study presents an improvement to robust ridge regression estimator. We proposed two methods Bisquare ridge least trimmed squares (BRLTS) and Bisquare ridge least absolute value (BRLAV) based on ridge least trimmed squares (RLTS) and ridge least absolute value (RLAV), respectively. We compared these methods with existing estimators, namely ordinary least squares (OLS) and Huber ridge regression (HRID) using three criteria: Bias, Root Mean Square Error (RMSE) and Standard Error (SE) to estimate the parameters coefficients. The results of Bisquare ridge least trimmed squares (BRLTS) and Bisquare ridge least absolute value (BRLAV) are compared with existing methods using real data and simulation study. The empirical evidence shows that the results obtain from the BRLTS are the best among the three estimators followed by BRLAV with the least value of the RMSE for the different disturbance distributions and degrees of multicollinearity. |
| doi_str_mv | 10.1063/1.4954633 |
| format | Conference Proceeding |
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J., Muhammad Alias</creator><contributor>Salleh, Shaharuddin ; Zainuddin, Zaitul Marlizawati ; Yusof, Yudariah Mohammad ; Aris, Nor’aini ; Lee, Muhammad Hisyam ; Bahar, Arifah ; Ahmad, Tahir ; Maan, Normah</contributor><creatorcontrib>Pati, Kafi Dano ; Adnan, Robiah ; Rasheed, Bello Abdulkadir ; MD. J., Muhammad Alias ; Salleh, Shaharuddin ; Zainuddin, Zaitul Marlizawati ; Yusof, Yudariah Mohammad ; Aris, Nor’aini ; Lee, Muhammad Hisyam ; Bahar, Arifah ; Ahmad, Tahir ; Maan, Normah</creatorcontrib><description>This study presents an improvement to robust ridge regression estimator. We proposed two methods Bisquare ridge least trimmed squares (BRLTS) and Bisquare ridge least absolute value (BRLAV) based on ridge least trimmed squares (RLTS) and ridge least absolute value (RLAV), respectively. We compared these methods with existing estimators, namely ordinary least squares (OLS) and Huber ridge regression (HRID) using three criteria: Bias, Root Mean Square Error (RMSE) and Standard Error (SE) to estimate the parameters coefficients. The results of Bisquare ridge least trimmed squares (BRLTS) and Bisquare ridge least absolute value (BRLAV) are compared with existing methods using real data and simulation study. The empirical evidence shows that the results obtain from the BRLTS are the best among the three estimators followed by BRLAV with the least value of the RMSE for the different disturbance distributions and degrees of multicollinearity.</description><identifier>ISSN: 0094-243X</identifier><identifier>EISSN: 1551-7616</identifier><identifier>DOI: 10.1063/1.4954633</identifier><identifier>CODEN: APCPCS</identifier><language>eng</language><publisher>Melville: American Institute of Physics</publisher><subject>Economic models ; Estimators ; Outliers (statistics) ; Parameter estimation ; Parameter robustness ; Regression ; Regression analysis ; Robustness (mathematics) ; Root-mean-square errors ; Standard error</subject><ispartof>AIP conference proceedings, 2016, Vol.1750 (1)</ispartof><rights>Author(s)</rights><rights>2016 Author(s). 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The empirical evidence shows that the results obtain from the BRLTS are the best among the three estimators followed by BRLAV with the least value of the RMSE for the different disturbance distributions and degrees of multicollinearity.</description><subject>Economic models</subject><subject>Estimators</subject><subject>Outliers (statistics)</subject><subject>Parameter estimation</subject><subject>Parameter robustness</subject><subject>Regression</subject><subject>Regression analysis</subject><subject>Robustness (mathematics)</subject><subject>Root-mean-square errors</subject><subject>Standard error</subject><issn>0094-243X</issn><issn>1551-7616</issn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2016</creationdate><recordtype>conference_proceeding</recordtype><recordid>eNp9kM1KAzEUhYMoWKsL3yDgTpiav0lmlrbUHygIWsFdmM7ctCnTyTTJKH0CX9upLbhzdRf3u-fccxC6pmREieR3dCTyVEjOT9CApilNlKTyFA0IyUXCBP84RxchrAlhuVLZAH1PQ7SbIlrX4LbwxQYi-IC7YJslHtuw7QoP-AvschWhwt4tuhCxt9USsIelhxD2p-PX2fwNw0HLeWwbHFeA234PTQnYGbzp6mhLV9e2gcLbuMNFU2HXxdr2jpfozBR1gKvjHKL3h-l88pTMXh6fJ_ezpOQsi4mhMi15vgBDcsXUoqQKDM8gZWUFwkjCoDIp7QmaV0ykIiNKGCpySagQsuRDdHPQbb3bdv3Deu063_SWmlFGFSMi4z11e6BCaeNvObr1fTS_05TofdGa6mPR_8Gfzv-Buq0M_wHP5YFA</recordid><startdate>20160621</startdate><enddate>20160621</enddate><creator>Pati, Kafi Dano</creator><creator>Adnan, Robiah</creator><creator>Rasheed, Bello Abdulkadir</creator><creator>MD. J., Muhammad Alias</creator><general>American Institute of Physics</general><scope>8FD</scope><scope>H8D</scope><scope>L7M</scope></search><sort><creationdate>20160621</creationdate><title>Estimation parameters using Bisquare weighted robust ridge regression BRLTS estimator in the presence of multicollinearity and outliers</title><author>Pati, Kafi Dano ; Adnan, Robiah ; Rasheed, Bello Abdulkadir ; MD. J., Muhammad Alias</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c328t-f165c39bef09727bc17ef38e52cde4f602edf5139b19d24548074f149601446c3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Economic models</topic><topic>Estimators</topic><topic>Outliers (statistics)</topic><topic>Parameter estimation</topic><topic>Parameter robustness</topic><topic>Regression</topic><topic>Regression analysis</topic><topic>Robustness (mathematics)</topic><topic>Root-mean-square errors</topic><topic>Standard error</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Pati, Kafi Dano</creatorcontrib><creatorcontrib>Adnan, Robiah</creatorcontrib><creatorcontrib>Rasheed, Bello Abdulkadir</creatorcontrib><creatorcontrib>MD. J., Muhammad Alias</creatorcontrib><collection>Technology Research Database</collection><collection>Aerospace Database</collection><collection>Advanced Technologies Database with Aerospace</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Pati, Kafi Dano</au><au>Adnan, Robiah</au><au>Rasheed, Bello Abdulkadir</au><au>MD. J., Muhammad Alias</au><au>Salleh, Shaharuddin</au><au>Zainuddin, Zaitul Marlizawati</au><au>Yusof, Yudariah Mohammad</au><au>Aris, Nor’aini</au><au>Lee, Muhammad Hisyam</au><au>Bahar, Arifah</au><au>Ahmad, Tahir</au><au>Maan, Normah</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Estimation parameters using Bisquare weighted robust ridge regression BRLTS estimator in the presence of multicollinearity and outliers</atitle><btitle>AIP conference proceedings</btitle><date>2016-06-21</date><risdate>2016</risdate><volume>1750</volume><issue>1</issue><issn>0094-243X</issn><eissn>1551-7616</eissn><coden>APCPCS</coden><abstract>This study presents an improvement to robust ridge regression estimator. We proposed two methods Bisquare ridge least trimmed squares (BRLTS) and Bisquare ridge least absolute value (BRLAV) based on ridge least trimmed squares (RLTS) and ridge least absolute value (RLAV), respectively. We compared these methods with existing estimators, namely ordinary least squares (OLS) and Huber ridge regression (HRID) using three criteria: Bias, Root Mean Square Error (RMSE) and Standard Error (SE) to estimate the parameters coefficients. The results of Bisquare ridge least trimmed squares (BRLTS) and Bisquare ridge least absolute value (BRLAV) are compared with existing methods using real data and simulation study. The empirical evidence shows that the results obtain from the BRLTS are the best among the three estimators followed by BRLAV with the least value of the RMSE for the different disturbance distributions and degrees of multicollinearity.</abstract><cop>Melville</cop><pub>American Institute of Physics</pub><doi>10.1063/1.4954633</doi><tpages>14</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0094-243X |
| ispartof | AIP conference proceedings, 2016, Vol.1750 (1) |
| issn | 0094-243X 1551-7616 |
| language | eng |
| recordid | cdi_scitation_primary_10_1063_1_4954633 |
| source | AIP Journals Complete |
| subjects | Economic models Estimators Outliers (statistics) Parameter estimation Parameter robustness Regression Regression analysis Robustness (mathematics) Root-mean-square errors Standard error |
| title | Estimation parameters using Bisquare weighted robust ridge regression BRLTS estimator in the presence of multicollinearity and outliers |
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