{"created":"2021-03-01T07:02:51.899220+00:00","id":42913,"links":{},"metadata":{"_buckets":{"deposit":"b8a69f38-f438-4f4e-891c-1f37a2b2cb2a"},"_deposit":{"id":"42913","owners":[],"pid":{"revision_id":0,"type":"depid","value":"42913"},"status":"published"},"_oai":{"id":"oai:repository.dl.itc.u-tokyo.ac.jp:00042913","sets":["62:7433:7434","9:7435:7436"]},"item_8_biblio_info_7":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2011-10","bibliographicIssueDateType":"Issued"},"bibliographicVolumeNumber":"CIRJE-F-823","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 best linear unbiased predictor (BLUP) is called a kriging predictor and has been widely used to interpolate a spatially correlated random process in scientific areas such as geostatistics. In many cases, data sets in spatial problems are often so large that a kriging predictor is impractically time-consuming. To reduce the computational complexity, covariance tapering has been developed by Furrer et al. (2006) for large spatial data sets. The BLUP is identical with the conditional expectation if an underlying random field is Gaussian and consequently is the optimal predictor in the mean squared error (MSE) sense whereas if an original data takes a nonnegative value or has a skewed distribution, we frequently apply a nonlinear transformation to it to get a data which is nearer Gaussian. Then the optimality of the BLUP for the original data is unclear because it is not Gaussian. In this paper we consider covariance tapering in a class of transformed Gaussian models for random fields and show that the BLUP, the BLUP using covariance tapering and the optimal predictor are asymptotically equivalent in the MSE sense if the covariance function of the underlying Gaussian random field is Mat´ern type. This is an extension of Furrer et al. (2006). Monte Carlo simulations support theoretical results.","subitem_description_type":"Abstract"}]},"item_8_description_6":{"attribute_name":"内容記述","attribute_value_mlt":[{"subitem_description":"Revised in July 2012, December 2012 and January 2013; forthcoming in Annals of Institute of Statistical Mathematics.","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/2011/2011cf823ab.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":"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":"Graduate School of 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":"Hirano, Toshihiro"}],"nameIdentifiers":[{"nameIdentifier":"98676","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Yajima, Yoshihiro"}],"nameIdentifiers":[{"nameIdentifier":"98677","nameIdentifierScheme":"WEKO"}]}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"Covariance tapering","subitem_subject_scheme":"Other"},{"subitem_subject":"Hermite polynomials","subitem_subject_scheme":"Other"},{"subitem_subject":"Kriging","subitem_subject_scheme":"Other"},{"subitem_subject":"Spatial statistics","subitem_subject_scheme":"Other"},{"subitem_subject":"Spectral density","subitem_subject_scheme":"Other"},{"subitem_subject":"Transformed random field","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":"Covariance Tapering for Prediction of Large Spatial Data Sets in Transformed Random Fields","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Covariance Tapering for Prediction of Large Spatial Data Sets in Transformed Random Fields"}]},"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":"42913","relation_version_is_last":true,"title":["Covariance Tapering for Prediction of Large Spatial Data Sets in Transformed Random Fields"],"weko_creator_id":"1","weko_shared_id":null},"updated":"2022-12-19T04:18:02.295623+00:00"}