Shrinkage Estimation Strategies in Generalised Ridge Regression Models: Low/High‐Dimension Regime
Summary In this study, we suggest pretest and shrinkage methods based on the generalised ridge regression estimation that is suitable for both multicollinear and high‐dimensional problems. We review and develop theoretical results for some of the shrinkage estimators. The relative performance of the...
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| Veröffentlicht in: | International statistical review 2020-04, Vol.88 (1), p.229-251 |
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| creator | Yüzbaşı, Bahadır Arashi, Mohammad Ejaz Ahmed, S. |
| description | Summary
In this study, we suggest pretest and shrinkage methods based on the generalised ridge regression estimation that is suitable for both multicollinear and high‐dimensional problems. We review and develop theoretical results for some of the shrinkage estimators. The relative performance of the shrinkage estimators to some penalty methods is compared and assessed by both simulation and real‐data analysis. We show that the suggested methods can be accounted as good competitors to regularisation techniques, by means of a mean squared error of estimation and prediction error. A thorough comparison of pretest and shrinkage estimators based on the maximum likelihood method to the penalty methods. In this paper, we extend the comparison outlined in his work using the least squares method for the generalised ridge regression. |
| doi_str_mv | 10.1111/insr.12351 |
| format | Article |
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In this study, we suggest pretest and shrinkage methods based on the generalised ridge regression estimation that is suitable for both multicollinear and high‐dimensional problems. We review and develop theoretical results for some of the shrinkage estimators. The relative performance of the shrinkage estimators to some penalty methods is compared and assessed by both simulation and real‐data analysis. We show that the suggested methods can be accounted as good competitors to regularisation techniques, by means of a mean squared error of estimation and prediction error. A thorough comparison of pretest and shrinkage estimators based on the maximum likelihood method to the penalty methods. In this paper, we extend the comparison outlined in his work using the least squares method for the generalised ridge regression.</description><identifier>ISSN: 0306-7734</identifier><identifier>EISSN: 1751-5823</identifier><identifier>DOI: 10.1111/insr.12351</identifier><language>eng</language><publisher>Hoboken: John Wiley & Sons, Inc</publisher><subject>Computer simulation ; Data analysis ; Estimators ; Generalised ridge regression ; Least squares method ; low‐dimensional and high‐dimensional data ; Maximum likelihood method ; multicollinearity ; penalty estimation ; Regression analysis ; Regression models ; Regularization ; Shrinkage ; shrinkage estimation</subject><ispartof>International statistical review, 2020-04, Vol.88 (1), p.229-251</ispartof><rights>2020 The Authors. International Statistical Review © 2020 International Statistical Institute</rights><rights>2020 International Statistical Institute</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3011-3654ab223a06bebd143e864c9bb56227a66729454af3ac1de30ff6c2cfe533493</citedby><cites>FETCH-LOGICAL-c3011-3654ab223a06bebd143e864c9bb56227a66729454af3ac1de30ff6c2cfe533493</cites><orcidid>0000-0002-6196-3201</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1111%2Finsr.12351$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1111%2Finsr.12351$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,780,784,1417,27924,27925,45574,45575</link.rule.ids></links><search><creatorcontrib>Yüzbaşı, Bahadır</creatorcontrib><creatorcontrib>Arashi, Mohammad</creatorcontrib><creatorcontrib>Ejaz Ahmed, S.</creatorcontrib><title>Shrinkage Estimation Strategies in Generalised Ridge Regression Models: Low/High‐Dimension Regime</title><title>International statistical review</title><description>Summary
In this study, we suggest pretest and shrinkage methods based on the generalised ridge regression estimation that is suitable for both multicollinear and high‐dimensional problems. We review and develop theoretical results for some of the shrinkage estimators. The relative performance of the shrinkage estimators to some penalty methods is compared and assessed by both simulation and real‐data analysis. We show that the suggested methods can be accounted as good competitors to regularisation techniques, by means of a mean squared error of estimation and prediction error. A thorough comparison of pretest and shrinkage estimators based on the maximum likelihood method to the penalty methods. In this paper, we extend the comparison outlined in his work using the least squares method for the generalised ridge regression.</description><subject>Computer simulation</subject><subject>Data analysis</subject><subject>Estimators</subject><subject>Generalised ridge regression</subject><subject>Least squares method</subject><subject>low‐dimensional and high‐dimensional data</subject><subject>Maximum likelihood method</subject><subject>multicollinearity</subject><subject>penalty estimation</subject><subject>Regression analysis</subject><subject>Regression models</subject><subject>Regularization</subject><subject>Shrinkage</subject><subject>shrinkage estimation</subject><issn>0306-7734</issn><issn>1751-5823</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNp90EtOwzAQBmALgUQpbDhBJHZIaW1P4qTsUOlLKiC1sLacZJK6pEmxU1XdcQTOyElwG9Z4FrbkzzPyT8gtoz3mVl9X1vQYh5CdkQ6LQuaHMYdz0qFAhR9FEFySK2vXlFLgcdAh6XJldPWhCvRGttEb1ei68paNUQ0WGq2nK2-CFRpVaouZt9CZowssDFp7pM91hqV98Ob1vj_Vxern6_tJb7A6XTrnztfkIlelxZu_vUvex6O34dSfv05mw8e5nwJlzAcRBirhHBQVCSYZCwBjEaSDJAkF55ESIuKDwKEcVMoyBJrnIuVpjiFAMIAuuWv7bk39uUPbyHW9M5UbKTnEsQipK6fuW5Wa2lqDudwa929zkIzKY4jyGKI8hegwa_Fel3j4R8rZy3LRvvkF1H51sw</recordid><startdate>202004</startdate><enddate>202004</enddate><creator>Yüzbaşı, Bahadır</creator><creator>Arashi, Mohammad</creator><creator>Ejaz Ahmed, S.</creator><general>John Wiley & Sons, Inc</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>H8D</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><orcidid>https://orcid.org/0000-0002-6196-3201</orcidid></search><sort><creationdate>202004</creationdate><title>Shrinkage Estimation Strategies in Generalised Ridge Regression Models: Low/High‐Dimension Regime</title><author>Yüzbaşı, Bahadır ; Arashi, Mohammad ; Ejaz Ahmed, S.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3011-3654ab223a06bebd143e864c9bb56227a66729454af3ac1de30ff6c2cfe533493</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Computer simulation</topic><topic>Data analysis</topic><topic>Estimators</topic><topic>Generalised ridge regression</topic><topic>Least squares method</topic><topic>low‐dimensional and high‐dimensional data</topic><topic>Maximum likelihood method</topic><topic>multicollinearity</topic><topic>penalty estimation</topic><topic>Regression analysis</topic><topic>Regression models</topic><topic>Regularization</topic><topic>Shrinkage</topic><topic>shrinkage estimation</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yüzbaşı, Bahadır</creatorcontrib><creatorcontrib>Arashi, Mohammad</creatorcontrib><creatorcontrib>Ejaz Ahmed, S.</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>Aerospace Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>International statistical review</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Yüzbaşı, Bahadır</au><au>Arashi, Mohammad</au><au>Ejaz Ahmed, S.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Shrinkage Estimation Strategies in Generalised Ridge Regression Models: Low/High‐Dimension Regime</atitle><jtitle>International statistical review</jtitle><date>2020-04</date><risdate>2020</risdate><volume>88</volume><issue>1</issue><spage>229</spage><epage>251</epage><pages>229-251</pages><issn>0306-7734</issn><eissn>1751-5823</eissn><abstract>Summary
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| ispartof | International statistical review, 2020-04, Vol.88 (1), p.229-251 |
| issn | 0306-7734 1751-5823 |
| language | eng |
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| source | Wiley Journals |
| subjects | Computer simulation Data analysis Estimators Generalised ridge regression Least squares method low‐dimensional and high‐dimensional data Maximum likelihood method multicollinearity penalty estimation Regression analysis Regression models Regularization Shrinkage shrinkage estimation |
| title | Shrinkage Estimation Strategies in Generalised Ridge Regression Models: Low/High‐Dimension Regime |
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