{"created":"2021-03-01T07:02:45.381038+00:00","id":42817,"links":{},"metadata":{"_buckets":{"deposit":"901ab4cf-80cd-4975-92d5-b8182204d15a"},"_deposit":{"id":"42817","owners":[],"pid":{"revision_id":0,"type":"depid","value":"42817"},"status":"published"},"_oai":{"id":"oai:repository.dl.itc.u-tokyo.ac.jp:00042817","sets":["62:7433:7434","9:7435:7436"]},"item_8_biblio_info_7":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2011-04","bibliographicIssueDateType":"Issued"},"bibliographicVolumeNumber":"CIRJE-F-801","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 empirical best linear unbiased predictor (EBLUP) in the linear mixed model (LMM) is useful for the small area estimation, and the estimation of the mean squared error (MSE) of EBLUP is important as a measure of uncertainty of EBLUP. To obtain a second-order unbiased estimator of the MSE, the second-order bias correction has been derived mainly based on Taylor series expansions. However, this approach is harder to implement in complicated models with more unknown parameters like variance components, since we need to compute asymptotic bias, variance and covariance for estimators of unknown parameters as well as partial derivatives of some quantities. The same difficulty occurs in construction of confidence intervals based on EBLUP with second-order correction and in derivation of second-order bias correction terms in the Akaike Information Criterion (AIC) and the conditional AIC. To avoid such difficulty in derivation of second-order bias correction in these problems, the parametric bootstrap methods are suggested in this paper, and their second-order justifications are established. Finally, performances of the suggested procedures are numerically investigated in comparison with some existing procedures given in the literature.","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/2011/2011cf801ab.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/10.1016/j.jmva.2011.12.002","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":"Faculty of Economics, University of Tokyo"},{"subitem_text_value":"Graduate School in Economics, University of Tokyo"}]},"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, Tatsuya"}],"nameIdentifiers":[{"nameIdentifier":"98443","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Nagashima, Bui"}],"nameIdentifiers":[{"nameIdentifier":"98444","nameIdentifierScheme":"WEKO"}]}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"Best linear unbiased predictor","subitem_subject_scheme":"Other"},{"subitem_subject":"confidence interval","subitem_subject_scheme":"Other"},{"subitem_subject":"empirical Bayes procedure","subitem_subject_scheme":"Other"},{"subitem_subject":"Fay-Herriot model","subitem_subject_scheme":"Other"},{"subitem_subject":"second-order correction","subitem_subject_scheme":"Other"},{"subitem_subject":"linear mixed model","subitem_subject_scheme":"Other"},{"subitem_subject":"maximum likelihood estimator","subitem_subject_scheme":"Other"},{"subitem_subject":"mean squared error","subitem_subject_scheme":"Other"},{"subitem_subject":"nested error regression model","subitem_subject_scheme":"Other"},{"subitem_subject":"parametric bootstrap","subitem_subject_scheme":"Other"},{"subitem_subject":"restricted maximum likelihood estimator","subitem_subject_scheme":"Other"},{"subitem_subject":"small area estimation","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":"Parametric Bootstrap Methods for Bias Correction in Linear Mixed Models","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Parametric Bootstrap Methods for Bias Correction in Linear Mixed Models"}]},"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":"42817","relation_version_is_last":true,"title":["Parametric Bootstrap Methods for Bias Correction in Linear Mixed Models"],"weko_creator_id":"1","weko_shared_id":null},"updated":"2022-12-19T04:17:59.754429+00:00"}