{"created":"2021-03-01T07:02:43.619406+00:00","id":42791,"links":{},"metadata":{"_buckets":{"deposit":"12a10720-debf-4787-9eb5-d35fb3778f2f"},"_deposit":{"id":"42791","owners":[],"pid":{"revision_id":0,"type":"depid","value":"42791"},"status":"published"},"_oai":{"id":"oai:repository.dl.itc.u-tokyo.ac.jp:00042791","sets":["62:7433:7434","9:7435:7436"]},"item_8_biblio_info_7":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2007-02","bibliographicIssueDateType":"Issued"},"bibliographicVolumeNumber":"CIRJE-F-472","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) has been used very successfully in the literature in model selection for small number of parameters pand large number of observations N. The cases when pis large and close to N or when p >N have not been considered in the literature. In fact, when pis large and close to N, the available AIC does not perform well at all. We consider these cases in the context of finding the number of components of the mean vector that may be different from zero in one-sample multivariate analysis. In fact, we consider this problem in more generality by considering it as a growth curve model introduced in Rao (1959) and Potthoff and Roy (1964). Using simulation, it has been shown that the proposed AIC procedures perform very well.","subitem_description_type":"Abstract"}]},"item_8_description_6":{"attribute_name":"内容記述","attribute_value_mlt":[{"subitem_description":"Revised in November 2007; subsequently published in Journal of the Japan Statistical Society (2008), 38, 259-283.","subitem_description_type":"Other"},{"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/2007/2007cf472ab.html","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":"334","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_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":"98393","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Kubokawa, Tatsuya"}],"nameIdentifiers":[{"nameIdentifier":"98394","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":"high correlation","subitem_subject_scheme":"Other"},{"subitem_subject":"high dimensional model","subitem_subject_scheme":"Other"},{"subitem_subject":"ridge estimator","subitem_subject_scheme":"Other"},{"subitem_subject":"selection of means","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":"Akaike Information Criterion for Selecting Components of the Mean Vector in High Dimensional Data with Fewer Observations","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Akaike Information Criterion for Selecting Components of the Mean Vector in High Dimensional Data with Fewer Observations"}]},"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":"42791","relation_version_is_last":true,"title":["Akaike Information Criterion for Selecting Components of the Mean Vector in High Dimensional Data with Fewer Observations"],"weko_creator_id":"1","weko_shared_id":null},"updated":"2022-12-19T04:17:43.517199+00:00"}