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High-dimensional semiparametric inference and machine learning

Research Project

Project/Area Number 15K00047
Research Category

Grant-in-Aid for Scientific Research (C)

Allocation TypeMulti-year Fund
Section一般
Research Field Statistical science
Research InstitutionChiba University (2018)
Shimane University (2015-2017)

Principal Investigator

Naito Kanta  千葉大学, 大学院理学研究院, 教授 (80304252)

Research Collaborator Yoshida Takuma  
Tamatani Mitsuru  
Notsu Akifumi  
Project Period (FY) 2015-04-01 – 2019-03-31
Project Status Completed (Fiscal Year 2018)
Budget Amount *help
¥4,550,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥1,050,000)
Fiscal Year 2018: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2017: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2016: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2015: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Keywordsセミパラメトリック / 関数推定 / 高次元 / 機械学習 / 回帰関数の推定 / 密度推定 / パターン認識 / 歪曲度 / ダイバージェンス / ヒルベルト空間 / 平滑化 / 再生核ヒルベルト空間 / 適合度検定 / カーネル法 / ノンパラメトリック回帰 / ロバスト
Outline of Final Research Achievements

Significant results have been obtained in each of three themes. In the theme "Pattern Recognition", asymptotic results for the naive canonical correlation coefficient have been developed, and a new statistical analysis based on the dilatation has been proposed. In the theme "Density Estimation", a robust version of local density estimation method has been proposed and its theoretical properties have been investigated. Furthermore, in the theme "Regression", an algorithm for regression based on the risk minimization has been considered and the performance of the resultant estimator has been clarified. Nonparametric kernel regression has been shown to work even in the setting where the explanatory variables are embedded into an unknown low dimensional manifold. A new method of nonlinear multivariate regression called the LMSR method has been proposed, and applied to analyze the development process of human fetuses.

Academic Significance and Societal Importance of the Research Achievements

学術的意義として、まず従来の統計解析手法をより広範なデータに適用可能とするための数理的拡張がなされた点が挙げられる。高次元データや外れ値を含むようなデータへの適用が可能となった。もう1点は、これまでになかった統計解析手法を構築した点である。特に、歪曲度を用いた多次元データの調和度解析や、多次元スタンダード曲線の構築法は、ヒト胎児の発生過程の解析を念頭に考案された。本研究で新たに考案されたこれらの手法により、ヒト胎児の臓器の発生について様々な知見を得ることができた点は、社会的意義となる。

Report

(5 results)
  • 2018 Annual Research Report   Final Research Report ( PDF )
  • 2017 Research-status Report
  • 2016 Research-status Report
  • 2015 Research-status Report
  • Research Products

    (20 results)

All 2019 2018 2017 2016 2014 Other

All Int'l Joint Research (5 results) Journal Article (5 results) (of which Peer Reviewed: 5 results,  Acknowledgement Compliant: 1 results) Presentation (10 results) (of which Int'l Joint Research: 2 results,  Invited: 1 results)

  • [Int'l Joint Research] University of New South Wales/Australia Mathematical Science Institute(オーストラリア)

    • Related Report
      2018 Annual Research Report
  • [Int'l Joint Research] University of New South Wales/University of Melbourne(Australia)

    • Related Report
      2017 Research-status Report
  • [Int'l Joint Research] University of New South Wales/University of Melbourne(Australia)

    • Related Report
      2016 Research-status Report
  • [Int'l Joint Research] University of New South Wales(Australia)

    • Related Report
      2015 Research-status Report
  • [Int'l Joint Research] University of Adelaide(Australia)

    • Related Report
      2015 Research-status Report
  • [Journal Article] Locally robust methods and near-parametric asymptotics.2018

    • Author(s)
      Spiridon Penev and Kanta Naito
    • Journal Title

      Journal of Multivariate Analysis

      Volume: 167 Pages: 395-417

    • DOI

      10.1016/j.jmva.2018.06.006

    • Related Report
      2018 Annual Research Report
    • Peer Reviewed
  • [Journal Article] The LMSR method for providing a multidimensional understanding of growth standard in human fetuses.2017

    • Author(s)
      Naito K, Shimizu S, Udagawa J, Otani H
    • Journal Title

      Stat Methods Med Res

      Volume: 2017 Issue: 9 Pages: 2809-2830

    • DOI

      10.1177/0962280216687339

    • Related Report
      2018 Annual Research Report
    • Peer Reviewed
  • [Journal Article] Data sharpening on unknown manifold2017

    • Author(s)
      Kudo Masaki、Naito Kanta
    • Journal Title

      Communications in Statistics - Theory and Methods

      Volume: 46 Issue: 23 Pages: 11721-11744

    • DOI

      10.1080/03610926.2016.1277756

    • NAID

      120006502554

    • Related Report
      2017 Research-status Report
    • Peer Reviewed
  • [Journal Article] Statistical analyses in trials for the comprehensive understanding of organogenesis and histogenesis in humans and mice2016

    • Author(s)
      Hiroki Otani, Jun Udagawa and Kanta Naito
    • Journal Title

      Journal of Biochemistry

      Volume: 未定 Issue: 6 Pages: 553-561

    • DOI

      10.1093/jb/mvw020

    • NAID

      40020869341

    • Related Report
      2015 Research-status Report
    • Peer Reviewed / Acknowledgement Compliant
  • [Journal Article] Statistical analysis with dilatation for development process of human fetuses2014

    • Author(s)
      Naito K, Notsu A, Udagawa J, Otani H
    • Journal Title

      Stat Methods Med Res

      Volume: - Issue: 1 Pages: 176-200

    • DOI

      10.1177/0962280214543405

    • Related Report
      2016 Research-status Report
    • Peer Reviewed
  • [Presentation] ダイバージェンスに基づく局所密度推定の漸近理論2019

    • Author(s)
      内藤貫太
    • Organizer
      研究集会「第20回ノンパラメトリック統計解析とベイズ統計」
    • Related Report
      2018 Annual Research Report
  • [Presentation] Regression with stagewise minimization on risk function.2018

    • Author(s)
      Kanta Naito
    • Organizer
      IMS-APRM 2018
    • Related Report
      2018 Annual Research Report
    • Int'l Joint Research
  • [Presentation] On nonparametric estimation of dilatation.2018

    • Author(s)
      Kanta Naito
    • Organizer
      統計関連学会連合大会
    • Related Report
      2018 Annual Research Report
  • [Presentation] 歪曲度のノンパラメトリック推定2018

    • Author(s)
      内藤貫太
    • Organizer
      科研費シンポジウム「多変量データ解析法における理論と応用」
    • Related Report
      2018 Annual Research Report
  • [Presentation] Regression with stagewise minimization on risk function2017

    • Author(s)
      内藤貫太
    • Organizer
      日本数学会
    • Related Report
      2017 Research-status Report
  • [Presentation] Locally robust density estimation and near-parametric asymptotics2017

    • Author(s)
      Kanta Naito, Spiridon Penev
    • Organizer
      統計関連学会連合大会
    • Related Report
      2017 Research-status Report
  • [Presentation] Divergenceに基づく局所密度推定2017

    • Author(s)
      川村健太、内藤貫太
    • Organizer
      統計関連学会連合大会
    • Related Report
      2017 Research-status Report
  • [Presentation] 再生核ヒルベルト空間における正規性の検定2017

    • Author(s)
      牧草夏実、内藤貫太
    • Organizer
      統計関連学会連合大会
    • Related Report
      2017 Research-status Report
  • [Presentation] Kernel Naive Bayes for High Dimensional Pattern Recognition2016

    • Author(s)
      内藤貫太
    • Organizer
      統計関連学会連合大会
    • Place of Presentation
      金沢大学
    • Year and Date
      2016-09-05
    • Related Report
      2016 Research-status Report
  • [Presentation] Kernel Naive Bayes for High Dimensional Pattern Recognition2016

    • Author(s)
      Kanta Naito
    • Organizer
      3rd IMS-APRM 2016
    • Place of Presentation
      Chinese University of Hong Kong
    • Year and Date
      2016-06-27
    • Related Report
      2016 Research-status Report
    • Int'l Joint Research / Invited

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Published: 2015-04-16   Modified: 2020-03-30  

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