{"created":"2021-03-01T07:02:48.030916+00:00","id":42856,"links":{},"metadata":{"_buckets":{"deposit":"382c1cb1-23ed-490d-9328-1a0a1c7a8b5d"},"_deposit":{"id":"42856","owners":[],"pid":{"revision_id":0,"type":"depid","value":"42856"},"status":"published"},"_oai":{"id":"oai:repository.dl.itc.u-tokyo.ac.jp:00042856","sets":["62:7433:7434","9:7435:7436"]},"item_8_biblio_info_7":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2010-01","bibliographicIssueDateType":"Issued"},"bibliographicVolumeNumber":"CIRJE-F-709","bibliographic_titles":[{"bibliographic_title":"Discussion paper series. CIRJE-F"}]}]},"item_8_description_13":{"attribute_name":"フォーマット","attribute_value_mlt":[{"subitem_description":"application/pdf","subitem_description_type":"Other"}]},"item_8_description_5":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"The Akaike information criterion, AIC, and Mallows'Cp statistic have been proposed for selecting a smaller number of regressor variables in the multivariate regression models with fully unknown covariance matrix. All these criteria are, however, based on the implicit assumption that the sample size is substantially larger than the dimension of the covariance matrix. To obtain a stable estimator of the covariance matrix, it is required that the dimension of the covariance matrix be much smaller than the sample size. When the dimension is close to the sample size, it is necessary to use ridge type of estimators for the covariance matrix. In this paper, we use a ridge type of estimators for the covariance matrix and obtain the modified AIC and modified Cp statistic under the asymptotic theory that both the sample size and the dimension go to infinity. It is numerically shown that these modified procedures perform very well in the sense of selecting the true model in large dimensional cases.","subitem_description_type":"Abstract"}]},"item_8_description_6":{"attribute_name":"内容記述","attribute_value_mlt":[{"subitem_description":"本文フィルはリンク先を参照のこと","subitem_description_type":"Other"}]},"item_8_publisher_20":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"日本経済国際共同センター"}]},"item_8_relation_25":{"attribute_name":"関係URI","attribute_value_mlt":[{"subitem_relation_type_id":{"subitem_relation_type_id_text":"http://www.cirje.e.u-tokyo.ac.jp/research/dp/2010/2010cf709ab.html","subitem_relation_type_select":"URI"}}]},"item_8_relation_29":{"attribute_name":"異版あり","attribute_value_mlt":[{"subitem_relation_type":"hasVersion","subitem_relation_type_id":{"subitem_relation_type_id_text":"http://doi.org/10.1080/03610926.2011.624242","subitem_relation_type_select":"URI"}}]},"item_8_source_id_10":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA11450569","subitem_source_identifier_type":"NCID"}]},"item_8_subject_15":{"attribute_name":"日本十進分類法","attribute_value_mlt":[{"subitem_subject":"335","subitem_subject_scheme":"NDC"}]},"item_8_text_21":{"attribute_name":"出版者別名","attribute_value_mlt":[{"subitem_text_value":"Center for International Research on the Japanese Economy"}]},"item_8_text_4":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"Department of Statistics, University of Toronto, CANADA"},{"subitem_text_value":"Faculty of Economics, University of Tokyo, JAPAN"}]},"item_access_right":{"attribute_name":"アクセス権","attribute_value_mlt":[{"subitem_access_right":"metadata only access","subitem_access_right_uri":"http://purl.org/coar/access_right/c_14cb"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Srivastava, Muni S."}],"nameIdentifiers":[{"nameIdentifier":"98523","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Kubokawa, Tatsuya"}],"nameIdentifiers":[{"nameIdentifier":"98524","nameIdentifierScheme":"WEKO"}]}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"Akaike information criterion","subitem_subject_scheme":"Other"},{"subitem_subject":"Mallows’Cp","subitem_subject_scheme":"Other"},{"subitem_subject":"large dimension","subitem_subject_scheme":"Other"},{"subitem_subject":"multivariate linear regression model","subitem_subject_scheme":"Other"},{"subitem_subject":"selection of variables","subitem_subject_scheme":"Other"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourcetype":"technical report","resourceuri":"http://purl.org/coar/resource_type/c_18gh"}]},"item_title":"Selection of Variables in Multivariate Regression Models for Large Dimensions","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Selection of Variables in Multivariate Regression Models for Large Dimensions"}]},"item_type_id":"8","owner":"1","path":["7436","7434"],"pubdate":{"attribute_name":"公開日","attribute_value":"2017-01-17"},"publish_date":"2017-01-17","publish_status":"0","recid":"42856","relation_version_is_last":true,"title":["Selection of Variables in Multivariate Regression Models for Large Dimensions"],"weko_creator_id":"1","weko_shared_id":null},"updated":"2022-12-19T04:18:00.061644+00:00"}