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POIGNARD Benjamin  POIGNARD BENJAMIN

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POIGNARD BENJAMIN  ポイニヤル ベンジヤミン

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Researcher Number 40845252
Other IDs
Affiliation (Current) 2025: 慶應義塾大学, 理工学部(矢上), 准教授
Affiliation (based on the past Project Information) *help 2025: 大阪大学, 大学院経済学研究科, 准教授
2022 – 2023: 大阪大学, 大学院経済学研究科, 准教授
2022: 大阪大学, 大学院経済学研究科, 講師
2019 – 2021: 大阪大学, 経済学研究科, 講師
Review Section/Research Field
Principal Investigator
Basic Section 07030:Economic statistics-related / 0107:Economics, business administration, and related fields
Except Principal Investigator
Basic Section 61030:Intelligent informatics-related
Keywords
Principal Investigator
High dimension / Time series / Asymptotic theory / Sparsity / Factor models / Copulas / Multivariate modelling / Factor model / Copula / Penalisation … More / Multivariate Time Series / High-dimension / M-estimation / High-dimensions / Financial econometrics … More
Except Principal Investigator
機械学習 / 統計的推論 / 木構造最適輸送距離 / カーネル法 / 電子透かし / 特徴選択 / 選択的推論 Less
  • Research Projects

    (4 results)
  • Research Products

    (44 results)
  • Co-Researchers

    (4 People)
  •  Statistical modelling for multivariate models of high dimensionPrincipal Investigator

    • Principal Investigator
      POIGNARD BENJAMIN
    • Project Period (FY)
      2025 – 2027
    • Research Category
      Grant-in-Aid for Early-Career Scientists
    • Review Section
      Basic Section 07030:Economic statistics-related
    • Research Institution
      The University of Osaka
  •  Sparse statistical approach for multivariate modellingPrincipal Investigator

    • Principal Investigator
      POIGNARD BENJAMIN
    • Project Period (FY)
      2022 – 2024
    • Research Category
      Grant-in-Aid for Early-Career Scientists
    • Review Section
      Basic Section 07030:Economic statistics-related
    • Research Institution
      Osaka University
  •  Research and development of nonlinear Selective Inference for high-dimensional and small number of samples data

    • Principal Investigator
      Yamada Makoto
    • Project Period (FY)
      2020 – 2023
    • Research Category
      Grant-in-Aid for Scientific Research (B)
    • Review Section
      Basic Section 61030:Intelligent informatics-related
    • Research Institution
      Okinawa Institute of Science and Technology Graduate University
      Kyoto University
  •  Parsimonious statistical modelling for high-dimensional problemsPrincipal Investigator

    • Principal Investigator
      POIGNARD BENJAMIN
    • Project Period (FY)
      2019 – 2022
    • Research Category
      Grant-in-Aid for Research Activity Start-up
    • Review Section
      0107:Economics, business administration, and related fields
    • Research Institution
      Osaka University

All 2024 2023 2022 2021 2020 2019

All Journal Article Presentation

  • [Journal Article] Sparse M-estimators in semi-parametric copula models2024

    • Author(s)
      Benjamin Poignard and Jean-David Fermanian
    • Journal Title

      Bernoulli

      Volume: Forthcoming

    • Peer Reviewed / Open Access / Int'l Joint Research
    • Data Source
      KAKENHI-PROJECT-22K13377
  • [Journal Article] High-Dimensional Nonlinear Feature Selection with Hilbert-Schmidt Independence Criterion Lasso2023

    • Author(s)
      山田 誠, Poignard Benjamin, 山田 宏暁, Freidling Tobias
    • Journal Title

      Journal of the Japan Statistical Society, Japanese Issue

      Volume: 53 Issue: 1 Pages: 49-67

    • DOI

      10.11329/jjssj.53.49

    • ISSN
      0389-5602, 2189-1478
    • Year and Date
      2023-09-07
    • Language
      Japanese
    • Peer Reviewed / Open Access / Int'l Joint Research
    • Data Source
      KAKENHI-PROJECT-21H04874, KAKENHI-PROJECT-20H04243
  • [Journal Article] Estimation of high-dimensional vector autoregression via sparse precision matrix2023

    • Author(s)
      Poignard Benjamin、Asai Manabu
    • Journal Title

      The Econometrics Journal

      Volume: -- Issue: 2 Pages: 307-326

    • DOI

      10.1093/ectj/utad003

    • Peer Reviewed / Int'l Joint Research
    • Data Source
      KAKENHI-PROJECT-22K01429, KAKENHI-PROJECT-22K13377
  • [Journal Article] Feature Screening with Kernel Knockoff2022

    • Author(s)
      Benjamin Poignard, Peter Naylor, Hector Climente, Makoto Yamada
    • Journal Title

      International Conference on Artificial Intelligence and Statistics (AISTATS)

      Volume: -

    • Peer Reviewed / Open Access
    • Data Source
      KAKENHI-PROJECT-20H04243
  • [Journal Article] Feature screening with kernel knockoffs2022

    • Author(s)
      Benjamin Poignard; Peter Naylor; Hector Climente-Gonzalez; Makoto Yamada
    • Journal Title

      Proceedings of Machine Learning Research, AISTATS 2022

      Volume: 151 Pages: 1935-1974

    • Peer Reviewed / Open Access / Int'l Joint Research
    • Data Source
      KAKENHI-PROJECT-19K23193
  • [Journal Article] High‐dimensional sparse multivariate stochastic volatility models2022

    • Author(s)
      Poignard Benjamin, Asai Manabu
    • Journal Title

      Journal of Time Series Analysis

      Volume: - Issue: 1 Pages: 00-00

    • DOI

      10.1111/jtsa.12647

    • Peer Reviewed / Open Access / Int'l Joint Research
    • Data Source
      KAKENHI-PROJECT-19K01594, KAKENHI-PROJECT-19K23193
  • [Journal Article] Feature screening with kernel knockoffs2022

    • Author(s)
      Benjamin Poignard、Peter J. Naylor、Hector Climente-Gonzalez、Makoto Yamada
    • Journal Title

      Proceedings of The 25th International Conference on Artificial Intelligence and Statistics

      Volume: 151 Pages: 1935-1974

    • Peer Reviewed / Open Access / Int'l Joint Research
    • Data Source
      KAKENHI-PROJECT-19K23193
  • [Journal Article] Post-selection inference with HSIC-Lasso2021

    • Author(s)
      Tobias Freidling, Benjamin Poignard, Hector Climente-Gonzalez, Makoto Yamada
    • Journal Title

      International Conference on Machine Learning (ICML)

      Volume: - Pages: 3439-3448

    • Peer Reviewed / Open Access / Int'l Joint Research
    • Data Source
      KAKENHI-PROJECT-20H04243
  • [Journal Article] Post-Selection Inference with HSIC-Lasso2021

    • Author(s)
      Tobias Freidling; Benjamin Poignard; Hector Climente-Gonzalez; Makoto Yamada
    • Journal Title

      Proceedings of Machine Learning Research, ICML 2021

      Volume: 139 Pages: 3439-3448

    • Peer Reviewed / Open Access / Int'l Joint Research
    • Data Source
      KAKENHI-PROJECT-19K23193
  • [Journal Article] The finite sample properties of sparse M-estimators with pseudo-observations2021

    • Author(s)
      Benjamin POIGNARD; Jean-David FERMANIAN
    • Journal Title

      Annals of the Institute of Statistical Mathematics

      Volume: To appear Issue: 1 Pages: 1-31

    • DOI

      10.1007/s10463-021-00785-4

    • Peer Reviewed / Int'l Joint Research
    • Data Source
      KAKENHI-PROJECT-19K23193
  • [Journal Article] Sparse Hilbert-Schmidt Independence Criterion Regression2020

    • Author(s)
      Benjamin POIGNARD; Makoto Yamada
    • Journal Title

      Proceedings of Machine Learning Research, AISTATS 2020

      Volume: 108 Pages: 538-548

    • Peer Reviewed / Open Access / Int'l Joint Research
    • Data Source
      KAKENHI-PROJECT-19K23193
  • [Journal Article] High-dimensional penalized ARCH processes2020

    • Author(s)
      Benjamin POIGNARD; Jean-David FERMANIAN
    • Journal Title

      Econometric Reviews

      Volume: 40 Issue: 1 Pages: 86-107

    • DOI

      10.1080/07474938.2020.1761153

    • Peer Reviewed / Int'l Joint Research
    • Data Source
      KAKENHI-PROJECT-19K23193
  • [Journal Article] Statistical analysis of sparse approximate factor models2020

    • Author(s)
      Poignard Benjamin、Terada Yoshikazu
    • Journal Title

      Electronic Journal of Statistics

      Volume: 14 Issue: 2 Pages: 3315-3365

    • DOI

      10.1214/20-ejs1745

    • Peer Reviewed / Open Access / Int'l Joint Research
    • Data Source
      KAKENHI-PROJECT-20K19756, KAKENHI-PROJECT-19K23193
  • [Journal Article] Sparse Hilbert-Schmidt Independence Criterion Regression2020

    • Author(s)
      Benjamin POIGNARD; Makoto YAMADA
    • Journal Title

      Proceedings of Machine Learning Research, AISTATS 2020

      Volume: To appear

    • Peer Reviewed / Open Access / Int'l Joint Research
    • Data Source
      KAKENHI-PROJECT-19K23193
  • [Presentation] Factor multivariate stochastic volatility models2024

    • Author(s)
      Benjamin Poignard
    • Organizer
      CREST (ENSAE Paris - IP Paris) - Finance & Financial Econometrics Seminar
    • Invited
    • Data Source
      KAKENHI-PROJECT-22K13377
  • [Presentation] Sparse M-estimator in semi-parametric copula models2023

    • Author(s)
      Benjamin Poignard
    • Organizer
      共同研究集会2023:接合関数(コピュラ)理論の新展開 - 統計数理研究所
    • Invited
    • Data Source
      KAKENHI-PROJECT-22K13377
  • [Presentation] Sparse factor model of high dimension2023

    • Author(s)
      Benjamin Poignard
    • Organizer
      10th International Congress on Industrial and Applied Mathematics - ICIAM 2023
    • Int'l Joint Research
    • Data Source
      KAKENHI-PROJECT-22K13377
  • [Presentation] Feature screening with kernel knockoffs2022

    • Author(s)
      Benjamin Poignard
    • Organizer
      AISTATS 2022 Conference
    • Int'l Joint Research
    • Data Source
      KAKENHI-PROJECT-19K23193
  • [Presentation] Asymptotic theory of sparse factor models in high-dimension2022

    • Author(s)
      Benjamin Poignard
    • Organizer
      International Conference on Econometrics and Statistics (EcoSta 2022)
    • Int'l Joint Research
    • Data Source
      KAKENHI-PROJECT-22K13377
  • [Presentation] Sparse M-estimator in semi-parametric copula models2022

    • Author(s)
      Benjamin POIGNARD
    • Organizer
      Statistical machine learning seminar, Institute of Statistical Mathematics
    • Invited
    • Data Source
      KAKENHI-PROJECT-19K23193
  • [Presentation] Sparse M-estimator in semi-parametric copula models2022

    • Author(s)
      Benjamin Poignard
    • Organizer
      Workshop of Copulas
    • Invited
    • Data Source
      KAKENHI-PROJECT-22K13377
  • [Presentation] Feature screening and knockoff filtering2022

    • Author(s)
      Benjamin POIGNARD
    • Organizer
      Mathematics seminar, Kyoto University, Graduate School of Informatics
    • Invited
    • Data Source
      KAKENHI-PROJECT-19K23193
  • [Presentation] Feature screening with kernel knockoffs2022

    • Author(s)
      Benjamin Poignard, Peter J. Naylor, Hector Climente-Gonzalez, Makoto Yamada
    • Organizer
      AISTATS 2022
    • Int'l Joint Research
    • Data Source
      KAKENHI-PROJECT-20H04243
  • [Presentation] Sparse M-estimator in semi-parametric copula models2022

    • Author(s)
      Benjamin Poignard
    • Organizer
      The 16th International Symposium on Econometric Theory and Applications: SETA2022
    • Int'l Joint Research
    • Data Source
      KAKENHI-PROJECT-22K13377
  • [Presentation] Feature screening with kernel knockoffs2022

    • Author(s)
      Benjamin POIGNARD
    • Organizer
      AISTATS 2022 conference
    • Int'l Joint Research
    • Data Source
      KAKENHI-PROJECT-19K23193
  • [Presentation] Asymptotic theory of sparse factor models in high-dimension2022

    • Author(s)
      Benjamin Poignard
    • Organizer
      International Conference on Econometrics and Statistics (EcoSta 2022)
    • Int'l Joint Research
    • Data Source
      KAKENHI-PROJECT-19K23193
  • [Presentation] Sparse factor models of high dimension2022

    • Author(s)
      Benjamin Poignard
    • Organizer
      CFE-CMStatistics 2022 Conference
    • Int'l Joint Research
    • Data Source
      KAKENHI-PROJECT-19K23193
  • [Presentation] Sparse M-estimator in semi-parametric copula models2022

    • Author(s)
      Benjamin Poignard
    • Organizer
      The 16th International Symposium on Econometric Theory and Applications: SETA2022
    • Int'l Joint Research
    • Data Source
      KAKENHI-PROJECT-19K23193
  • [Presentation] Sparse factor models of high dimension2022

    • Author(s)
      Benjamin Poignard
    • Organizer
      CFE-CMStatistics 2022 Conference
    • Int'l Joint Research
    • Data Source
      KAKENHI-PROJECT-22K13377
  • [Presentation] An overview of screening methods for feature selection2021

    • Author(s)
      Benjamin POIGNARD
    • Organizer
      Statistics Summer Seminar
    • Invited
    • Data Source
      KAKENHI-PROJECT-19K23193
  • [Presentation] Sparse M-estimator in semi-parametric copula models2021

    • Author(s)
      Benjamin POIGNARD
    • Organizer
      Nakanoshima Workshop - Osaka University
    • Invited
    • Data Source
      KAKENHI-PROJECT-19K23193
  • [Presentation] Estimation of High Dimensional Vector Autoregression via Sparse Precision Matrix2021

    • Author(s)
      Benjamin POIGNARD
    • Organizer
      Finance Seminar MMDS - Osaka University
    • Invited
    • Data Source
      KAKENHI-PROJECT-19K23193
  • [Presentation] Estimation of High Dimensional Vector Autoregression via Sparse Precision Matrix2021

    • Author(s)
      Benjamin POIGNARD
    • Organizer
      Finance Seminar - Ritsumeikan University
    • Invited
    • Data Source
      KAKENHI-PROJECT-19K23193
  • [Presentation] Sparse Hilbert-Schmidt Independence Regression Criterion2021

    • Author(s)
      Benjamin POIGNARD
    • Organizer
      Riken AIP - 9th AIP Open Seminar
    • Invited
    • Data Source
      KAKENHI-PROJECT-19K23193
  • [Presentation] Sparse Factor Models: Asymptotic Properties2021

    • Author(s)
      Benjamin POIGNARD
    • Organizer
      CFE-CMStatistics 2021 Conference
    • Int'l Joint Research
    • Data Source
      KAKENHI-PROJECT-19K23193
  • [Presentation] Sparse Factor Models: Asymptotic Properties2021

    • Author(s)
      Benjamin POIGNARD
    • Organizer
      Ecodep Conference 2021
    • Invited
    • Data Source
      KAKENHI-PROJECT-19K23193
  • [Presentation] Sparse Hilbert-Schmidt Independence Regression Criterion2020

    • Author(s)
      Benjamin POIGNARD
    • Organizer
      AISTATS 2020 conference
    • Int'l Joint Research
    • Data Source
      KAKENHI-PROJECT-19K23193
  • [Presentation] High-dimensional Sparse Multivariate Stochastic Volatility Models2020

    • Author(s)
      Benjamin POIGNARD
    • Organizer
      Seminar presentation at the 4th Asian Quantitative Finance Seminar
    • Int'l Joint Research
    • Data Source
      KAKENHI-PROJECT-19K23193
  • [Presentation] Sparse Hilbert-Schmidt Independence Regression Criterion2020

    • Author(s)
      Benjamin POIGNARD
    • Organizer
      Seminar presentation at the Institute of Scientific and Industrial Research of Osaka University
    • Invited
    • Data Source
      KAKENHI-PROJECT-19K23193
  • [Presentation] High-dimensional Sparse Multivariate Stochastic Volatility Models2020

    • Author(s)
      Benjamin POIGNARD
    • Organizer
      CFE-CMStatistics 2020 Conference
    • Int'l Joint Research
    • Data Source
      KAKENHI-PROJECT-19K23193
  • [Presentation] Long term asset allocation2019

    • Author(s)
      Benjamin POIGNARD
    • Organizer
      Marunochi Quantitative Finance Seminar
    • Invited
    • Data Source
      KAKENHI-PROJECT-19K23193
  • [Presentation] The Finite Sample Properties of Sparse M-estimators with Pseudo-Observations2019

    • Author(s)
      Benjamin POIGNARD
    • Organizer
      EcoSta
    • Int'l Joint Research
    • Data Source
      KAKENHI-PROJECT-19K23193
  • [Presentation] Statistical analysis of sparse approximate factor models2019

    • Author(s)
      Benjamin POIGNARD
    • Organizer
      SETA International Conference
    • Int'l Joint Research
    • Data Source
      KAKENHI-PROJECT-19K23193
  • [Presentation] Sparse Hilbert-Schmidt Independence Criterion Regression2019

    • Author(s)
      Benjamin POIGNARD
    • Organizer
      MMDS Seminar, Osaka University
    • Invited
    • Data Source
      KAKENHI-PROJECT-19K23193
  • 1.  Yamada Makoto (00581323)
    # of Collaborated Projects: 1 results
    # of Collaborated Products: 3 results
  • 2.  下平 英寿 (00290867)
    # of Collaborated Projects: 1 results
    # of Collaborated Products: 0 results
  • 3.  浅井 学
    # of Collaborated Projects: 0 results
    # of Collaborated Products: 2 results
  • 4.  寺田 吉壱
    # of Collaborated Projects: 0 results
    # of Collaborated Products: 1 results

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