Detecting influential observations in Liu and modified Liu estimators
In regression, detecting anomalous observations is a significant step for model-building process. Various influence measures based on different motivational arguments are designed to measure the influence of observations through different aspects of various regression models. The presence of influen...
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| Veröffentlicht in: | Journal of applied statistics 2013-08, Vol.40 (8), p.1735-1745 |
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| container_issue | 8 |
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| container_title | Journal of applied statistics |
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| creator | Ertas, Hasan Erisoglu, Murat Kaciranlar, Selahattin |
| description | In regression, detecting anomalous observations is a significant step for model-building process. Various influence measures based on different motivational arguments are designed to measure the influence of observations through different aspects of various regression models. The presence of influential observations in the data is complicated by the existence of multicollinearity. The purpose of this paper is to assess the influence of observations in the Liu [9] and modified Liu [15] estimators by using the method of approximate case deletion formulas suggested by Walker and Birch [14]. A numerical example using a real data set used by Longley [10] and a Monte Carlo simulation are given to illustrate the theoretical results. |
| doi_str_mv | 10.1080/02664763.2013.794203 |
| format | Article |
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Various influence measures based on different motivational arguments are designed to measure the influence of observations through different aspects of various regression models. The presence of influential observations in the data is complicated by the existence of multicollinearity. The purpose of this paper is to assess the influence of observations in the Liu [9] and modified Liu [15] estimators by using the method of approximate case deletion formulas suggested by Walker and Birch [14]. 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Various influence measures based on different motivational arguments are designed to measure the influence of observations through different aspects of various regression models. The presence of influential observations in the data is complicated by the existence of multicollinearity. The purpose of this paper is to assess the influence of observations in the Liu [9] and modified Liu [15] estimators by using the method of approximate case deletion formulas suggested by Walker and Birch [14]. A numerical example using a real data set used by Longley [10] and a Monte Carlo simulation are given to illustrate the theoretical results.</description><subject>Applied statistics</subject><subject>Approximation</subject><subject>Computer simulation</subject><subject>Deletion</subject><subject>diagnostics</subject><subject>Estimating techniques</subject><subject>Estimators</subject><subject>influential observations</subject><subject>Liu estimator</subject><subject>Mathematical models</subject><subject>modified Liu estimator</subject><subject>Monte Carlo methods</subject><subject>Monte Carlo simulation</subject><subject>multicollinearity</subject><subject>Numerical analysis</subject><subject>Regression</subject><subject>Regression analysis</subject><subject>Studies</subject><issn>0266-4763</issn><issn>1360-0532</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><recordid>eNp9kDtPwzAQgC0EEqXwDxgisbCknGMndieESnlIlVhgthznglwlcbEdEP8eh8LCwHTS3Xevj5BzCgsKEq6gqCouKrYogLKFWPIC2AGZUVZBDiUrDslsQvKJOSYnIWwBQNKSzcj6FiOaaIfXzA5tN-IQre4yVwf07zpaN4RUyDZ2zPTQZL1rbGux-U5giLbX0flwSo5a3QU8-4lz8nK3fl495Jun-8fVzSY3HGjMKROmEcZIzZuC1lToGkDoAiS2lAMKwRFpUxi-LCspESXQEktWF1A2om3ZnFzu5-68exvTftXbYLDr9IBuDIpWgjIpKsoSevEH3brRD-k6lVZBBSWXPFF8TxnvQvDYqp1PP_lPRUFNbtWvWzW5VXu3qe1635acOd_rD-e7RkX92Tnfej0YGxT7d8IX3Q5-8Q</recordid><startdate>20130801</startdate><enddate>20130801</enddate><creator>Ertas, Hasan</creator><creator>Erisoglu, Murat</creator><creator>Kaciranlar, Selahattin</creator><general>Taylor & Francis</general><general>Taylor & Francis Ltd</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></search><sort><creationdate>20130801</creationdate><title>Detecting influential observations in Liu and modified Liu estimators</title><author>Ertas, Hasan ; Erisoglu, Murat ; Kaciranlar, Selahattin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c401t-137cd7cc8a4d21b17ab007a208ef140e774ee1d2c495688ee8015e53b205d7ff3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Applied statistics</topic><topic>Approximation</topic><topic>Computer simulation</topic><topic>Deletion</topic><topic>diagnostics</topic><topic>Estimating techniques</topic><topic>Estimators</topic><topic>influential observations</topic><topic>Liu estimator</topic><topic>Mathematical models</topic><topic>modified Liu estimator</topic><topic>Monte Carlo methods</topic><topic>Monte Carlo simulation</topic><topic>multicollinearity</topic><topic>Numerical analysis</topic><topic>Regression</topic><topic>Regression analysis</topic><topic>Studies</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ertas, Hasan</creatorcontrib><creatorcontrib>Erisoglu, Murat</creatorcontrib><creatorcontrib>Kaciranlar, Selahattin</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>Journal of applied statistics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ertas, Hasan</au><au>Erisoglu, Murat</au><au>Kaciranlar, Selahattin</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Detecting influential observations in Liu and modified Liu estimators</atitle><jtitle>Journal of applied statistics</jtitle><date>2013-08-01</date><risdate>2013</risdate><volume>40</volume><issue>8</issue><spage>1735</spage><epage>1745</epage><pages>1735-1745</pages><issn>0266-4763</issn><eissn>1360-0532</eissn><abstract>In regression, detecting anomalous observations is a significant step for model-building process. 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| source | Business Source Complete (BSC) 商管财经类全文数据库(完整版) |
| subjects | Applied statistics Approximation Computer simulation Deletion diagnostics Estimating techniques Estimators influential observations Liu estimator Mathematical models modified Liu estimator Monte Carlo methods Monte Carlo simulation multicollinearity Numerical analysis Regression Regression analysis Studies |
| title | Detecting influential observations in Liu and modified Liu estimators |
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