Incomplete Data Analysis for Economic Statistics

• Material Type: 博士論文
• Title: Incomplete Data Analysis for Economic Statistics
• Author/Editor: 高橋, 将宜; Takahashi, Masayoshi
• Author Heading: 高橋, 将宜; Takahashi, Masayoshi
• Publication Date: 2017-10-13
• Publication Date (W3CDTF): 2017-10-13
• Alternative Title: 経済統計のための不完全データ解析
• Degree grantor/type: 成蹊大学
• Date Granted: 2017-09-06
• Date Granted (W3CDTF): 2017-09-06
• Dissertation Number: 乙第85号
• Degree Type: 博士（理工学）
• Conferring No. (Dissertation): 乙第85号
• Text Language Code: eng
• Subject Heading: Missing data; multiple imputation; ratio imputation; official statistics; 欠測データ; 欠損; 多重代入法; 比率代入法; 補完; 補定; 公的統計
• Target Audience: 一般
• Note (General): type:Thesis; Incomplete data are ubiquitous in social sciences; as a consequence, available data are inefficient and often biased. This dissertation deals with the problem of missing data in official economic statistics. Building on the practices of the United Nations Economic Commission for Europe (UNECE), the first half of the dissertation focuses on single imputation methods. After revealing that single ratio imputation is often used for economic data in the current practices of official statistics, this study unifies the three ratio imputation models under the framework of weighted least squares and proposes a novel estimation strategy for selecting a ratio imputation model based on the magnitude of heteroskedasticity. After showing that multiple imputation is suited for public-use microdata, the latter half of the dissertation focuses on multiple imputation methods. From a new perspective, this dissertation compares the three computational algorithms for multiple imputation: Data Augmentation (DA), Fully Conditional Specification (FCS), and Expectation-Maximization with Bootstrapping (EMB). It is found that EMB is a confidence proper multiple imputation algorithm without between-imputation iterations, meaning that EMB is more user-friendly than DA and FCS. Based on these findings, the current study proposes a novel application of the EMB algorithm to ratio imputation in order to create multiple ratio imputation, the new multiple imputation version of ratio imputation, providing brand-new software MrImputation implemented in R. Combining all of these findings, this dissertation will be an important addition to the literature of missing data analysis and official economic statistics.; 成蹊大学大学院 理工学研究科 理工学専攻情報科学コース; 論文主査名: 岩崎 学; identifier:http://repository.seikei.ac.jp/dspace/handle/10928/951
• DOI: 10.15018/00001215; https://doi.org/10.15018/00001215
• Persistent ID (NDL): info:ndljp/pid/10981830; https://dl.ndl.go.jp/pid/10981830
• Collection: 障害者向け資料
• Collection (Materials For Handicapped People:1): テキストデータ
• Collection (particular): 国立国会図書館デジタルコレクション > デジタル化資料 > 博士論文; https://dl.ndl.go.jp/collections/A00014
• Acquisition Basis: 博士論文（自動収集）
• Date Accepted (W3CDTF): 2017-11-03T04:45:49+09:00
• Format (IMT): application/pdf
• Access Restrictions: 国立国会図書館内限定公開
• Service for the Digitized Contents Transmission Service: 図書館・個人送信対象外
• Availability of remote photoduplication service: 可
• Periodical Title (URI): http://dx.doi.org/10.15018/00001215; http://hdl.handle.net/10928/951
• Data Provider (Database): 国立国会図書館 : 国立国会図書館デジタルコレクション; https://dl.ndl.go.jp

Table of Contents

http://repository.seikei.ac.jp/dspace/bitstream/10928/951/1/hakuron-takahashi.pdf fulltext

http://repository.seikei.ac.jp/dspace/bitstream/10928/951/3/hakuronshinsa-takahashi.pdf fulltext

http://repository.seikei.ac.jp/dspace/bitstream/10928/951/2/hakuronyoushi-takahashi.pdf fulltext