Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/104961
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dc.titleA paradox in least-squares estimation of linear regression models
dc.contributor.authorBai, Z.D.
dc.contributor.authorGuo, M.
dc.date.accessioned2014-10-28T05:09:28Z
dc.date.available2014-10-28T05:09:28Z
dc.date.issued1999-04-01
dc.identifier.citationBai, Z.D.,Guo, M. (1999-04-01). A paradox in least-squares estimation of linear regression models. Statistics and Probability Letters 42 (2) : 167-174. ScholarBank@NUS Repository.
dc.identifier.issn01677152
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/104961
dc.description.abstractThis note considers a paradox arising in the least-squares estimation of linear regression models in which the error terms are assumed to be i.i.d. and possess finite rth moment, for r ∈e [1,2). We give a concrete example to show that the least-squares estimator of the slope parameter is inconsistent when the intercept parameter of the model is given. However, surprisingly this estimator is consistent when the intercept parameter is intendedly assumed to be unknown and re-estimated simultaneously with the slope parameter. © 1999 Elsevier Science B.V. All rights reserved.
dc.sourceScopus
dc.subjectConsistency
dc.subjectLeast-squares estimate
dc.subjectRth moment
dc.typeArticle
dc.contributor.departmentSTATISTICS & APPLIED PROBABILITY
dc.description.sourcetitleStatistics and Probability Letters
dc.description.volume42
dc.description.issue2
dc.description.page167-174
dc.description.codenSPLTD
dc.identifier.isiutNOT_IN_WOS
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