{"created":"2021-03-01T07:02:19.641187+00:00","id":42437,"links":{},"metadata":{"_buckets":{"deposit":"00642e50-9212-46d0-aff2-b16e71768720"},"_deposit":{"id":"42437","owners":[],"pid":{"revision_id":0,"type":"depid","value":"42437"},"status":"published"},"_oai":{"id":"oai:repository.dl.itc.u-tokyo.ac.jp:00042437","sets":["62:7433:7434","9:7435:7436"]},"item_8_biblio_info_7":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2002-09","bibliographicIssueDateType":"Issued"},"bibliographicVolumeNumber":"2002-CF-170","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":"In this paper, we consider the problem of estimating the regression parameters in a multiple linear regression model with design matrix A when the multicollinearity is present. Minimax empirical Bayes estimators are proposed under the assumption of normality and loss function (δ-s)t (At A)2 (δ- s)/σ2, where δ is an estimator of the vector s of p regression parameters, and σ2 is the unknown variance of the model. The minimax estimators are also obtained under linear constraints on s such as s = Cα for some p × q known matrix C, q p. For a particular C, this combines the principal component regression and ridge regression. These results are also applicable for estimating the p means θi when the p observations xi are independently distributed as N (θi, diσ2), di's are known butσ2 is unknown.","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/2002/2002cf170ab.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":"330","subitem_subject_scheme":"NDC"}]},"item_8_text_17":{"attribute_name":"Mathmatical Subject Classification","attribute_value_mlt":[{"subitem_text_value":"62J05"},{"subitem_text_value":"62J07"},{"subitem_text_value":"62F10"},{"subitem_text_value":"62C12"},{"subitem_text_value":"62C20"}]},"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":"University of Tokyo"},{"subitem_text_value":"University of Toronto"}]},"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":"Kubokawa, Tetsuya"}],"nameIdentifiers":[{"nameIdentifier":"97677","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"M., S.  Srivastava"}],"nameIdentifiers":[{"nameIdentifier":"97678","nameIdentifierScheme":"WEKO"}]}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"Multi regression","subitem_subject_scheme":"Other"},{"subitem_subject":"multicolliearity","subitem_subject_scheme":"Other"},{"subitem_subject":"ridge regression","subitem_subject_scheme":"Other"},{"subitem_subject":"ernpirical Bayes method","subitem_subject_scheme":"Other"},{"subitem_subject":"principal component metod","subitem_subject_scheme":"Other"},{"subitem_subject":"hednic regression minimaxity","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":"Minimax Empirical Bayes Ridge-Principal Component Regression Estimators","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Minimax Empirical Bayes Ridge-Principal Component Regression Estimators"}]},"item_type_id":"8","owner":"1","path":["7436","7434"],"pubdate":{"attribute_name":"公開日","attribute_value":"2013-06-03"},"publish_date":"2013-06-03","publish_status":"0","recid":"42437","relation_version_is_last":true,"title":["Minimax Empirical Bayes Ridge-Principal Component Regression Estimators"],"weko_creator_id":"1","weko_shared_id":null},"updated":"2022-12-19T04:17:37.763290+00:00"}