| Project/Area Number |
22300096
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Statistical science
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| Research Institution | Osaka University |
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Principal Investigator |
KANO YUTAKA 大阪大学, 基礎工学研究科, 教授 (20201436)
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| Co-Investigator(Kenkyū-buntansha) |
DEGUCHI Yasuo 京都大学, 文学研究科, 准教授 (20314073)
WASHIO Takashi 大阪大学, 産業科学研究所, 教授 (00192815)
HAMAZAKI Toshimitsu 大阪大学, 医学系研究科, 准教授 (40379243)
TAKAGI Yoshiji 奈良教育大学, 教育学部, 准教授 (00231390)
SUGIMOTO Tomoyuki 弘前大学, 理工学研究科, 准教授 (70324829)
TAKAI Keiji 関西大学, 商学部, 准教授 (20572019)
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| Co-Investigator(Renkei-kenkyūsha) |
NAITO Kanta 島根大学, 総合理工学部, 教授 (80304252)
SHIMIZU Shohei 大阪大学, 産業科学研究所, 准教授 (10509871)
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| Research Collaborator |
KATAYAMA Shota 日本学術振興会, 特別研究員DC1
YAMAMOTO Michio 日本学術振興会, 特別研究員DC2
SONG Xinyuan The Chinese University of Hong Kong, HK, Professor
JAMSHIDIAN Mortaza California State University, Fullerton, USA Professor
HYVARINEN Aapo University of Helsinki, Finland, Professor
YUAN Ke-hai University of Notre Dame, USA, Professor
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| Project Period (FY) |
2010-04-01 – 2014-03-31
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| Project Status |
Completed (Fiscal Year 2013)
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| Budget Amount *help |
¥17,810,000 (Direct Cost: ¥13,700,000、Indirect Cost: ¥4,110,000)
Fiscal Year 2013: ¥4,940,000 (Direct Cost: ¥3,800,000、Indirect Cost: ¥1,140,000)
Fiscal Year 2012: ¥3,640,000 (Direct Cost: ¥2,800,000、Indirect Cost: ¥840,000)
Fiscal Year 2011: ¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2010: ¥4,810,000 (Direct Cost: ¥3,700,000、Indirect Cost: ¥1,110,000)
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| Keywords | shared-parameter モデル / 統計的因果推論 / 無視できない欠測 / NMAR / LiNGAM / 潜在交絡変数 / 2重中途打ち切り / 因果と予測 / 高次元データ / 大量欠測 / 潜在変数モデル / 人口データ解析 / 統計教育 / 無視可能性 / 欠測値問題とMAR / 補助変数 / 交絡変数 / リスク / ランダムな欠測 / 因果と欠測 / NMARness / Approximate Population Bias / ベイズ推測 / 推定方程式の不偏性 / 強い意味で無視可能 |
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| Research Abstract |
Analysis of incomplete data has been troublesome both theoretically and practically. In particular nonignorable missingness has been a serious issue in statistics. An alternative perspective of the theory of missing data analysis is to provide an insightful view of statistical causal inference. Some notable research outcomes include development of the analysis of doubly censored data, a new method of exploring causal structure for data with latent confounders via the LiNGAM approach, incomplete data analysis with a shared-parameter model and development of the EM algorithm with constraints.
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