{"created":"2021-03-01T07:06:08.409358+00:00","id":27801,"links":{},"metadata":{"_buckets":{"deposit":"56f1c870-3bf9-4d95-af45-4803f0070e7b"},"_deposit":{"id":"27801","owners":[],"pid":{"revision_id":0,"type":"depid","value":"27801"},"status":"published"},"_oai":{"id":"oai:tsukuba.repo.nii.ac.jp:00027801","sets":["117:1697","117:786","3:62:5601:1659"]},"item_5_biblio_info_6":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2011-11","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"4","bibliographicPageEnd":"399","bibliographicPageStart":"356","bibliographicVolumeNumber":"30","bibliographic_titles":[{"bibliographic_title":"Sequential analysis"}]}]},"item_5_creator_3":{"attribute_name":"著者別名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"青嶋, 誠"}],"nameIdentifiers":[{},{},{}]},{"creatorNames":[{"creatorName":"矢田, 和善"}],"nameIdentifiers":[{},{},{}]}]},"item_5_description_4":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"In this article, we consider a variety of inference problems for high-dimensional data. The purpose of this article is to suggest directions for future research and possible solutions about p  n problems by using new types of two-stage estimation methodologies. This is the first attempt to apply sequential analysis to high-dimensional statistical inference ensuring prespecified accuracy. We offer the sample size determination for inference problems by creating new types of multivariate two-stage procedures. To develop theory and methodologies, the most important and basic idea is the asymptotic normality when p → ∞. By developing asymptotic normality when p → ∞, we first give (a) a given-bandwidth confidence region for the square loss. In addition, we give (b) a two-sample test to assure prespecified size and power simultaneously together with (c) an equality-test procedure for two covariance matrices. We also give (d) a two-stage discriminant procedure that controls misclassification rates being no more than a prespecified value. Moreover, we propose (e) a two-stage variable selection procedure that provides screening of variables in the first stage and selects a significant set of associated variables from among a set of candidate variables in the second stage. Following the variable selection procedure, we consider (f) variable selection for high-dimensional regression to compare favorably with the lasso in terms of the assurance of accuracy and the computational cost. Further, we consider variable selection for classification and propose (g) a two-stage discriminant procedure after screening some variables. Finally, we consider (h) pathway analysis for high-dimensional data by constructing a multiple test of correlation coefficients.","subitem_description_type":"Abstract"}]},"item_5_description_5":{"attribute_name":"内容記述","attribute_value_mlt":[{"subitem_description":"Editor's Special Invited Paper。\nこの招待論文は、Abraham Wald Prize in Sequential Analysis 2012の受賞論文となっております。","subitem_description_type":"Other"}]},"item_5_identifier_34":{"attribute_name":"URI","attribute_value_mlt":[{"subitem_identifier_type":"HDL","subitem_identifier_uri":"http://hdl.handle.net/2241/117753"}]},"item_5_publisher_27":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"Taylor & Francis"}]},"item_5_relation_11":{"attribute_name":"DOI","attribute_value_mlt":[{"subitem_relation_type_id":{"subitem_relation_type_id_text":"10.1080/07474946.2011.619088","subitem_relation_type_select":"DOI"}}]},"item_5_relation_37":{"attribute_name":"関係URI","attribute_value_mlt":[{"subitem_relation_name":[{"subitem_relation_name_text":"http://hdl.handle.net/2241/117756"}]}]},"item_5_rights_12":{"attribute_name":"権利","attribute_value_mlt":[{"subitem_rights":"© Taylor & Francis Group, LLC. \nThis is an Author's Accepted Manuscript of an article published in Sequential Analysis Nov 2011 , available online at: http://www.tandfonline.com/doi/full/10.1080/07474946.2011.619088"}]},"item_5_select_15":{"attribute_name":"著者版フラグ","attribute_value_mlt":[{"subitem_select_item":"author"}]},"item_5_source_id_7":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"0747-4946","subitem_source_identifier_type":"ISSN"}]},"item_5_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA10538981","subitem_source_identifier_type":"NCID"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Aoshima, Makoto"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Yata, Kazuyoshi"}],"nameIdentifiers":[{}]}]},"item_files":{"attribute_name":"ファイル情報","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_date","date":[{"dateType":"Available","dateValue":"2013-12-25"}],"displaytype":"detail","filename":"SA_30-4-432.pdf","filesize":[{"value":"96.7 kB"}],"format":"application/pdf","licensetype":"license_note","mimetype":"application/pdf","url":{"label":"SA_30-4-432.pdf","url":"https://tsukuba.repo.nii.ac.jp/record/27801/files/SA_30-4-432.pdf"},"version_id":"d3551e6b-1cf9-4643-8949-2c53c1a2abc1"},{"accessrole":"open_date","date":[{"dateType":"Available","dateValue":"2013-12-25"}],"displaytype":"detail","filename":"SA_30-4-356.pdf","filesize":[{"value":"1.1 MB"}],"format":"application/pdf","licensetype":"license_note","mimetype":"application/pdf","url":{"label":"SA_30-4-356.pdf","url":"https://tsukuba.repo.nii.ac.jp/record/27801/files/SA_30-4-356.pdf"},"version_id":"b3afe9e3-dabd-43bb-b880-86742a00bf0c"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"Asymptotic normality","subitem_subject_scheme":"Other"},{"subitem_subject":"Classification","subitem_subject_scheme":"Other"},{"subitem_subject":"Confidence region","subitem_subject_scheme":"Other"},{"subitem_subject":"HDLSS","subitem_subject_scheme":"Other"},{"subitem_subject":"Lasso","subitem_subject_scheme":"Other"},{"subitem_subject":"Pathway analysis","subitem_subject_scheme":"Other"},{"subitem_subject":"Regression","subitem_subject_scheme":"Other"},{"subitem_subject":"Sample size determination","subitem_subject_scheme":"Other"},{"subitem_subject":"Testing equality of covariance matrices","subitem_subject_scheme":"Other"},{"subitem_subject":"Two-sample test","subitem_subject_scheme":"Other"},{"subitem_subject":"Variable selection","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":"journal article","resourceuri":"http://purl.org/coar/resource_type/c_6501"}]},"item_title":"Two-Stage Procedures for High-Dimensional Data","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Two-Stage Procedures for High-Dimensional Data"}]},"item_type_id":"5","owner":"1","path":["786","1697","1659"],"pubdate":{"attribute_name":"公開日","attribute_value":"2012-11-07"},"publish_date":"2012-11-07","publish_status":"0","recid":"27801","relation_version_is_last":true,"title":["Two-Stage Procedures for High-Dimensional Data"],"weko_creator_id":"1","weko_shared_id":5},"updated":"2022-04-27T08:55:47.240592+00:00"}