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J-GLOBAL ID:201401094873742472   Update date: Jan. 30, 2024

HENMI Masayuki

ヘンミ マサユキ | HENMI Masayuki
Affiliation and department:
Job title: Associate Professor
Other affiliations (1):
Research field  (1): Statistical science
Research keywords  (4): Information geometry ,  Semiparametric inference ,  Missing data analysis ,  Meta-analysis
Research theme for competitive and other funds  (9):
  • 2019 - 2024 統計科学のための情報幾何的方法の深化と発展
  • 2018 - 2022 多変量メタアナリシスにおける公表バイアスに対する感度解析
  • 2015 - 2019 統計的推論の情報幾何の新しい展開
  • 2013 - 2018 Statistical methodology for stabilizing of the genome-omics data analysis
  • 2013 - 2016 Stantistical researches for risk management in food microbial standards under the uncertainty caused by categorical random variables
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Papers (21):
  • Daisuke Yoneoka, Katsuhiro Omae, Masayuki Henmi, Shinto Eguchi. Area under the curve-optimized synthesis of prediction models from a meta-analytical perspective. Research Synthesis Methods. 2022
  • Masayuki Henmi, Satoshi Hattori, Tim Friede. A confidence interval robust to publication bias for random-effects meta-analysis of few studies. Research Synthesis Methods. 2021. 12. 5. 674-679
  • Daisuke Yoneoka, Masayuki Henmi. Clinical heterogeneity in random-effect meta-analysis: Between-study boundary estimate problem. Statistics in medicine. 2019. 38. 21. 4131-4145
  • Masayuki Henmi, Hiroshi Matsuzoe. Statistical Manifolds Admitting Torsion and Partially Flat Spaces. Geometric Structures of Information. 2019. 37-50
  • Hiroaki Tomita, Hironori Fujisawa, Masayuki Henmi. A bias-corrected estimator in multiple imputation for missing data. Statistics in Medicine. 2018. 37. 23. 3373-3386
more...
MISC (4):
  • YONEOKA Daisuke, HENMI Masayuki. Meta-analysis of logistic regression coefficients for clinical prediction models. Journal of Epidemiology. 2015. 25. Supplement 1
  • 米岡大輔, 逸見昌之. 連続量とその離散化を考慮した回帰モデルのメタアナリシス. 統計関連学会連合大会講演報告集. 2015. 2015
  • 逸見 昌之. 推定関数と捩れを許す統計多様体 (統計多様体の幾何学の新展開). 数理解析研究所講究録. 2014. 1916. 18-36
  • Hiroshi Matsuzoe, Masayuki Henmi. Hessian Structures and Divergence Functions on Deformed Exponential Families. Geometric Theory of Information: Signals and Communication Technology. 2014. 57-80
Books (1):
  • Data Science and Machine Learning
    2022
Lectures and oral presentations  (6):
  • Infinite-dimensional information geometry for statistics
    (The Royal Statistical Society 2023 International Conference (RSS2023) 2023)
  • Information Geometry associated with estimating functions
    (The Royal Statistical Society 2022 International Conference (RSS2022) 2022)
  • Understanding and Utilizing the Linearity Condition in Dimension Reduction
    (The 11th ICSA International Conference 2019)
  • An improved Henmi-Copas confidence interval for random effects meta-analysis with small number of studies
    (3rd International Conference on Econometrics and Statistics (EcoSta2019) 2019)
  • Statistical Manifolds Admitting Torsion, Pre-contrast Functions and Estimating Functions
    (3rd International Conference Geometric Science of Information (GSI2017) 2017)
more...
Education (1):
  • 2001 - 2004 The Graduate University for Advanced Studies Department of Statistical Science
Professional career (1):
  • 博士(学術) (総合研究大学院大学)
Work history (6):
  • 2018/04 - 現在 The Institute of Statistical Mathematics Department of Statistical Inference and Mathematics Associate Professor
  • 2012/04 - 2018/03 The Institute of Statistical Mathematics Department of Data Science Associate Professor
  • 2011/04 - 2012/03 The Institute of Statistical Mathematics Department of Mathematical Analysis and Statistical Inference Associate Professor
  • 2007/08 - 2011/03 The Institute of Statistical Mathematics Department of Mathematical Analysis and Statistical Inference Assistant Professor
  • 2007/06 - 2007/07 The Institute of Statistical Mathematics Risk Analysis Research Center Research Fellow
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Awards (1):
  • 2008 - The Biometric Society of Japan encouraging prize
Association Membership(s) (3):
The Royal Statistical Society ,  The Biometric Society of Japan ,  The Japan Statistical Society
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