{"created":"2023-07-27T07:53:26.680512+00:00","id":29233,"links":{},"metadata":{"_buckets":{"deposit":"86054de2-bc0b-45a7-b6dd-3157b95d23b2"},"_deposit":{"created_by":1,"id":"29233","owners":[1],"pid":{"revision_id":0,"type":"depid","value":"29233"},"status":"published"},"_oai":{"id":"oai:doshisha.repo.nii.ac.jp:00029233","sets":["4151:8699:4185:4186","4251:7158:7202:8771:7203"]},"author_link":["30775"],"control_number":"29233","item_1693813220986":{"attribute_name":"出版タイプ","attribute_value_mlt":[{"subitem_version_resource":"http://purl.org/coar/version/c_970fb48d4fbd8a85","subitem_version_type":"VoR"}]},"item_1694497428290":{"attribute_name":"学位名","attribute_value_mlt":[{"subitem_degreename":"博士(文化情報学)","subitem_degreename_language":"ja"}]},"item_1694497507610":{"attribute_name":"学位名（英）","attribute_value_mlt":[{"subitem_degreename":"Doctor of Culture and Information Science","subitem_degreename_language":"en"}]},"item_1694497573527":{"attribute_name":"学位授与機関","attribute_value_mlt":[{"subitem_degreegrantor":[{"subitem_degreegrantor_language":"ja","subitem_degreegrantor_name":"同志社大学"},{"subitem_degreegrantor_language":"en","subitem_degreegrantor_name":"Doshisha University"}],"subitem_degreegrantor_identifier":[{"subitem_degreegrantor_identifier_name":"34310","subitem_degreegrantor_identifier_scheme":"kakenhi"}]}]},"item_3_alternative_title_2":{"attribute_name":"その他（別言語等）のタイトル","attribute_value_mlt":[{"subitem_alternative_title":"構造的潜在変数モデリングに基づく複雑な線形回帰","subitem_alternative_title_language":"ja"},{"subitem_alternative_title":"コウゾウテキ センザイ ヘンスウ モデリング ニモトズク フクザツナ センケイ カイキ","subitem_alternative_title_language":"ja-Kana"}]},"item_3_date_granted_27":{"attribute_name":"学位授与年月日","attribute_value_mlt":[{"subitem_dategranted":"2022-09-17"}]},"item_3_description_12":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"The content discusses the importance of models in problem-solving, focusing on linear models. Linear models' strengths lie in their simplicity and applicability, yet they require modifications for complex real-world issues. The dissertation aims to contribute to multivariate linear model literature with two key proposals. Chapter 2 introduces a modified Deming Regression, dealing with multiple variables and errors. Chapter 3 delves into Structural Equation Modeling (SEM), addressing its challenges and proposing a K-means Generalized Maximum Entropy method. This enhances SEM's efficiency by using k-means centroids. Each chapter is outlined, detailing their significance and methodologies, and simulation results are presented. 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