1999 Fiscal Year Final Research Report Summary
Statistical Methods of Micro data Analysis
| Project/Area Number |
08209121
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| Research Category |
Grant-in-Aid for Scientific Research on Priority Areas
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| Allocation Type | Single-year Grants |
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| Research Institution | AOYAMA GAKUIN UNIVERSITY |
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Principal Investigator |
YOSHIZOE Yasuto Aoyama Gakuin University, Faculty of Economics, Professor, 経済学部, 教授 (80062868)
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| Co-Investigator(Kenkyū-buntansha) |
IWASAKI Manabu Seikei University, Faculty of Technology, Professor, 工学部, 教授 (40255948)
OJIMA Yoshikazu Science University of Tokyo, School of Management, Assistant Professor, 理工学部, 助教授 (50169299)
MIURA Yuki Surugadai University, Faculty of Economics, Professor, 経済学部, 教授 (30229645)
YAMAMOTO Wataru The University of Electro-Communications, The Graduate School of Information Systems, Research Associate, 大学院・システム学研究科, 助手
TSUBAKI Hiroe Tsukuba University, Institute of Policy and Planning Science, Assistant Professor, 社会工学系, 助教授 (30155436)
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| Project Period (FY) |
1996 – 1998
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| Keywords | micro data / incomplete data / statistical matching / bootstrap / data linkage / 有限母集団 / 再標本抽出 / ブートストラップ |
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| Research Abstract |
A practical problem of economic statistics is divided into (1) collection / processing stage of data and (2) an analysis stage. In particular you must examine reliability about observation when you analyze the micro data. The typical problem is to cope with incomplete data supplementing with external information. In this study, investigate technique to analyze the incomplete data which is supplemented by statistical matching.
1. Some theory models and an experiment of statiscal matching based on the government examine statistics to do so, me compare complete matching and statiscal matching and investigate a possibility of disclosure of micro data to the public.
2. As examples of incomplete data are missing values, termination of data collection, the curancy, and grouping of continuous data. And aecrecy of the individual information is a kind of incomplete data. In particular, we investigate the processing method of missing values.
3. An application of bootstrap method to the case of incomplete data. Suggest a simple non parametric method of bootstrap to supplement incomplete data.
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