{"created":"2021-03-01T07:33:03.533161+00:00","id":52484,"links":{},"metadata":{"_buckets":{"deposit":"bdc440dd-5412-4066-b1a6-e892af4e6956"},"_deposit":{"id":"52484","owners":[],"pid":{"revision_id":0,"type":"depid","value":"52484"},"status":"published"},"_oai":{"id":"oai:tsukuba.repo.nii.ac.jp:00052484","sets":["117:786","3:62:5619:730"]},"item_5_biblio_info_6":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2018-09","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicPageEnd":"111","bibliographicPageStart":"89","bibliographicVolumeNumber":"48","bibliographic_titles":[{"bibliographic_title":"日本統計学会誌"}]}]},"item_5_description_4":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"本論文は，高次元統計解析の理論と方法論について，最新の展開を紹介する．最近，Aoshima and Yata (2018a) は，強スパイク固有値(Strongly Spiked Eigenvalue: SSE)モデルというノイズモデルを提唱した．高次元データのノイズは巨大かつ非スパースであり，それゆえデータがもつ潜在的な幾何学的構造は破壊され，統計的推測に精度を保証することが困難になる．理論的には，SSEモデルのもとでは，高次元統計解析の根幹を成す高次元漸近正規性が成立しない．Aoshima and Yata (2018a) は，巨大なノイズ構造を精密に解析し，強スパイクするノイズ空間を避けるようなデータ変換法を開発した．この方法を用いれば，データは弱スパイク固有値(Non-SSE: NSSE)モデルに変換され，潜在空間の幾何学的構造が浮き彫りになり，高精度な高次元統計的推測が可能になる．Aoshima and Yata (2018b) は，この方法論を発展させ，高次元判別分析に新たな理論を展開している．本論文は，高次元統計解析の最新の展開について，適宜文献を紹介しながら解説する.","subitem_description_type":"Abstract"}]},"item_5_identifier_34":{"attribute_name":"URI","attribute_value_mlt":[{"subitem_identifier_type":"HDL","subitem_identifier_uri":"http://hdl.handle.net/2241/00157905"}]},"item_5_publisher_27":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"一般社団法人 日本統計学会"}]},"item_5_relation_11":{"attribute_name":"DOI","attribute_value_mlt":[{"subitem_relation_type_id":{"subitem_relation_type_id_text":"10.11329/jjssj.48.89","subitem_relation_type_select":"DOI"}}]},"item_5_rights_12":{"attribute_name":"権利","attribute_value_mlt":[{"subitem_rights":"© 2018 日本統計学会"}]},"item_5_select_15":{"attribute_name":"著者版フラグ","attribute_value_mlt":[{"subitem_select_item":"publisher"}]},"item_5_source_id_7":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"0389-5602","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":"AA11989749","subitem_source_identifier_type":"NCID"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"青嶋, 誠"},{"creatorName":"アオシマ, マコト","creatorNameLang":"ja-Kana"},{"creatorName":"AOSHIMA, Makoto","creatorNameLang":"en"}],"nameIdentifiers":[{},{},{}]}]},"item_files":{"attribute_name":"ファイル情報","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_date","date":[{"dateType":"Available","dateValue":"2019-10-21"}],"displaytype":"detail","filename":"JJSSJI_48-1.pdf","filesize":[{"value":"1.8 MB"}],"format":"application/pdf","licensetype":"license_note","mimetype":"application/pdf","url":{"label":"JJSSJI_48-1","url":"https://tsukuba.repo.nii.ac.jp/record/52484/files/JJSSJI_48-1.pdf"},"version_id":"000102a3-1b02-4a21-b732-7ce13839f9bf"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"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":"高次元統計解析: 理論と方法論の新しい展開","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"高次元統計解析: 理論と方法論の新しい展開"}]},"item_type_id":"5","owner":"1","path":["786","730"],"pubdate":{"attribute_name":"公開日","attribute_value":"2019-10-21"},"publish_date":"2019-10-21","publish_status":"0","recid":"52484","relation_version_is_last":true,"title":["高次元統計解析: 理論と方法論の新しい展開"],"weko_creator_id":"1","weko_shared_id":5},"updated":"2022-04-27T09:25:58.269611+00:00"}