Minami, Mihoko

写真a

Affiliation

Faculty of Science and Technology, Department of Mathematics (Yagami)

Position

Professor

Related Websites
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Career 【 Display / hide 】

  • 1982.04
    -
    1987.11

    Japan Univac Co.Ltd.

  • 1995.04
    -
    1999.03

    Science University of Tokyo, Faculty of Science, Department of Applied Mathematics, Assistant Professor

  • 1999.04
    -
    2009.03

    The Institute of Statistical Mathematics, Department of Mathematical Analysis and Statistical Inference, Associate Professor

  • 1999.04
    -
    2009.03

    The Graduate University for Advanced Studies, School of Multidisciplinary Sciences, Department of Statistical Science, Associate Professor

  • 2001.09
    -
    2008.03

    Keiou University, Faculty of Science and Technology, Part-time Lecturer

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Academic Background 【 Display / hide 】

  • 1982.03

    Ochanomizu University, Faculty of Science, Department of Mathematics

    University, Graduated

  • 1990.06

    University of California, San Diego Master course, Department of Mathematics

    United States, University, Graduated

  • 1993.12

    University of California, San Diego Department of Mathematics, Ph.D. course, Department of Mathematics

    United States, University, Graduated

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Academic Degrees 【 Display / hide 】

  • Ph.D., University of California, San Diego, Coursework, 1993.12

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Research Areas 【 Display / hide 】

  • Informatics / Statistical science (Statistical Science)

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Research Themes 【 Display / hide 】

  • 環境リスク解析, 

    2009.04
    -
    Present

  • Cyclic regression smoothing spline for environmental data, 

    2009
    -
    Present

  • Regression and Classification problem for distributions, 

    2008
    -
    Present

  • Feature extraction method from very non-normal data, 

    2007
    -
    Present

    　View Summary

    海洋生物の混獲数データのように非正規性の強いデータから特徴量を抽出する統計手法の研究。視点をかえると非正規データの次元の削減問題と捉えることができる

  • 生物資源評価, 

    2005.04
    -
    Present

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Books 【 Display / hide 】

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Papers 【 Display / hide 】

  • Covariate Selection Strategy for the Extended Propensity Score to Adjust for Missing Not at Random Data

    Shintaro Yoneyama, Mihoko Minami

    International Journal of Statistics and Probability (Canadian Center of Science and Education)  13 ( 4 ) 26 - 41 2024.11

    Research paper (scientific journal), Joint Work, Last author, Accepted,  ISSN  1927-7040

    　View Summary

    Abstract
    Missing data can introduce biases in the estimation of the indicator of interest if appropriate adjustments are not made. The case of Missing Not at Random (MNAR), a missing mechanism in which the missingness also depends on the missing values themselves, has been under-explored. When an outcome has MNAR data, one method to estimate the population mean of the outcome is using the extended propensity score. This method first estimates the extended propensity score, which is the missing probability conditional on the outcome and covariates. Then, the population mean of the outcome is estimated using these estimates. In this paper, we discuss which variables should be included in or excluded from the extended propensity score model to obtain an unbiased estimate of the population mean with small standard errors. First, we show which covariates, at a minimum, should be included in the model of missing probability so that the population mean estimator of the outcome is consistent. Next, we show that the inclusion of some covariates in the missing probability model results in a large variance of the population mean estimates even if they explain the missing probability well. Then, we verify these arguments using simulation experiments and argue that to obtain unbiased, small-variance estimates of the population mean, it is desirable to include only those covariates necessary for consistency. This study allows us to obtain such estimates when the outcome is MNAR and adjusted by the extended propensity score.

  • Regression Tree and Clustering for Distributions, and Homogeneous Structure of Population Characteristics

    Mihoko Minami and Cleridy E. Lennert-Cody

    Journal of Agricultural, Biological and Environmental Statistics  2024.06

    Research paper (scientific journal), Joint Work, Lead author, Last author, Corresponding author, Accepted,  ISSN  10857117

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    Scientists often collect samples on characteristics of different observation units and wonder whether those characteristics have similar distributional structure. We consider methods to find homogeneous subpopulations in a multidimensional space using regression tree and clustering methods for distributions of a population characteristic. We present a new methodology to estimate a standardized measure of distance between clusters of distributions and for hierarchical testing to find the minimal homogeneous or near-homogeneous tree structure. In addition, we introduce hierarchical clustering with adjacency constraints, which is useful for clustering georeferenced distributions. We conduct simulation studies to compare clustering performance with three measures: Modified Jensen–Shannon divergence (MJS), Earth Mover’s distance and Cramér–von Mises distance to validate the proposed testing procedure for homogeneity. As a motivational example, we introduce georeferenced yellowfin tuna fork length data collected from the catch of purse-seine vessels that operated in the eastern Pacific Ocean. Hierarchical clustering, with and without spatial adjacency constraints, and regression tree methods were applied to the density estimates of length. While the results from the two methods showed some similarities, hierarchical clustering with spatial adjacency produced a more flexible partition structure, without requiring additional covariate information. Clustering with MJS produced more stable results than clustering with the other measures.

  • Treatment Effects Estimation with Missing not at Random Data Without Outcome Modeling

    Yoneyama S., Minami M.

    Journal of Statistical Theory and Practice (Journal of Statistical Theory and Practice)  17 ( 3 )  2023.09

    Research paper (scientific journal), Joint Work, Last author, Accepted,  ISSN  15598608

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    When we study treatment effects in observational studies, we often face two problems: confounding and missing data. To properly estimate the treatment effects with missing data, we need to solve both problems simultaneously. When the outcome is missing not at random (MNAR), existing methods for estimating treatment effects require outcome modeling. However, identifying an appropriate model for a variable with MNAR is difficult. In this paper, we propose a method to estimate the average treatment effect (ATE), the average treatment effect on the treated (ATT), and the average treatment effect on the untreated (ATU) using an instrumental variable for missing data without modeling the outcome when it is MNAR. Here, the instrumental variable is independent of the potential outcome and associated with the missingness, when conditioned on the covariates. We show that the proposed estimators for ATE, ATT, and ATU are consistent and asymptotically normal. In addition, we demonstrate that the proposed estimators perform better in terms of bias and root-mean-squared error than other commonly used missing data adjustment methods. Moreover, the variance formula of the proposed estimator is verified through simulated experiments. This study allows us to estimate causal effects without bias and without modeling the outcome when the outcome is MNAR.

  • Flickering flash signals and mate recognition in the Asian firefly, Aquatica lateralis

    Hideo Takatsu, Mihoko Minami, Yuichi Oba

    Scientific Reports (Nature)  13 ( 2415 )  2023.02

    Research paper (scientific journal), Joint Work, Accepted

    　View Summary

    Nocturnal fireflies sometimes use intricate bioluminescent signal systems for sexual communication. In this study, we examined flash signals and mate recognition in the Asian firefly, Aquatica lateralis, under natural field conditions. We found that the flash pattern of females changes after copulation, from simple short flashes to flashes with longer duration and flickering. To understand the functions of flickering, we video-recorded and analyzed the flashes of sedentary males, receptive females, and mated females. The results showed that the flashes of these three adult phases can be discriminated from each other by two parameters, flash duration and flicker intensity, with little overlap. Male attraction experiments using an artificial LED device termed ‘e-firefly’ confirmed that flying and sedentary males are attracted to flashes with shorter durations and lower flicker intensities. The range of attraction success was much wider for flying males and narrower for sedentary males, and the latter was close to the range of receptive female’s flashes. These findings suggest that in addition to flash duration, flicker intensity is a flash signal parameter of mate recognition in A. lateralis males.

  • Exposure to PM2.5 and Lung Function Growth in Pre- and Early-Adolescent Schoolchildren A Longitudinal Study Involving Repeated Lung Function Measurements in Japan

    Toru Takebayashi, Masataka Taguri, Hiroshi Odajima et al.

    Annals of the American Thoracic Society (Annals of the American Thoracic Society)  19 ( 5 ) 763 - 772 2022.05

    Research paper (scientific journal), Joint Work, Accepted,  ISSN  23296933

    　View Summary

    Rationale: Epidemiological evidence indicates that ambient exposure to particulate matter <2.5 μm in aerodynamic diameter (PM2.5) has adverse effects on lung function growth in children, but it is not actually clear whether exposure to low-level PM2.5 results in long-term decrements in lung function growth in preto early-adolescent schoolchildren. Objectives: To examine long-term effects of PM2.5 within the 4-year average concentration range of 10-19 μg/m3 on lung function growth with repeated measurements of lung function tests. Methods: Longitudinal analysis of 6,233 lung function measurements in 1,466 participants aged 8-12 years from 16 school communities in 10 cities around Japan, covering a broad area of the country to represent concentration ranges of PM2.5, was done with a multilevel linear regression model. Forced expiratory volume in 1 second, forced vital capacity (FVC), and maximal expiratory flow at 50% of FVC were used as lung function indicators to examine the effects of 10-μg/m3 increases in the PM2.5 concentration on relative growth per each 10-cm increase in height. Results: The overall annual mean PM2.5 level was 13.5 μg/m3 (range, 10.4-19.0 μg/m3). We found no association between any of the lung function growth indicators and increases in PM2.5 levels in children of either sex, even after controlling for potential confounders. Analysis with two-pollutant models with O3 or NO2 did not change the null results. Conclusions: This nationwide longitudinal study suggests that concurrent, long-term exposure to PM2.5 at concentrations ranging from 10.4 to 19.0 μg/m3 has little effect on lung function growth in preadolescent boys or pre- to early-adolescent girls.

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Papers, etc., Registered in KOARA 【 Display / hide 】

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Presentations 【 Display / hide 】

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Research Projects of Competitive Funds, etc. 【 Display / hide 】

  • 分布データの解析手法，統計的推測法の提案と生物資源評価，生態・環境データへの応用

    2021.04
    -
    2026.03

    MEXT,JSPS, Grant-in-Aid for Scientific Research, Grant-in-Aid for Scientific Research (C), Principal investigator

  • 環境リスク解析・生物資源評価のための統計的モデリングと解析手法

    2017.04
    -
    2021.03

    独立行政法人 日本学術振興会, Grant-in-Aid for Scientific Research, Research grant, Principal investigator

    　View Summary

    本研究は，環境リスク解析と生物資源評価のための，統計的モデリングと解析手法の提案を目的とする．

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Awards 【 Display / hide 】

  • Jacob Wolfowitz prize

    Mihoko Minami and Kunio Shimizu, 2001.12, American Journal of Mathematical and Management Sciences, ML and REML estimation of Matusita's measure for two bivariate normal distributions with missing observations

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Courses Taught 【 Display / hide 】

  • TOPICS IN STATISTICAL SCIENCES A

    2024

  • TOPICS IN LIFE INSURANCE MATHEMATICS

    2024

  • STATISTICAL SCIENCE AND ITS EXERCISE

    2024

  • SEMINAR IN STATISTICAL SCIENCES

    2024

  • INTRODUCTION TO STATISTICAL SCIENCE

    2024

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Courses Previously Taught 【 Display / hide 】

  • データサイエンス特別講義

    慶應義塾大学理工学研究科

    2019.04
    -
    2020.03
    , 

    Autumn Semester

  • 数理統計学第二

    慶應義塾大学理工学部

    2019.04
    -
    2020.03
    , 

    Autumn Semester

  • 数学2B

    慶應義塾大学理工学部

    2019.04
    -
    2020.03
    , 

    Autumn Semester

  • 統計科学特論Ａ

    Keio University

    2014.04
    -
    2015.03
    , 

    Spring Semester, Lecture, Within own ｆaculty, 1h

  • 数理統計学第一同演習

    Keio University

    2014.04
    -
    2015.03
    , 

    Spring Semester, Lecture, Within own ｆaculty, 1h

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Social Activities 【 Display / hide 】

  • 微小粒子状物質等疫学調査研究検討会 環境省 水・大気環境局

    2010.09
    -
    Present

  • AISM 編集委員会

    2006.04
    -
    2014.03
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Memberships in Academic Societies 【 Display / hide 】

  • 国際計量生物学会 IBC2012 実行委員会, 

    2009.11
    -
    2012.09

  • 国際計量生物学会 IBC2010 国際プログラム委員会, 

    2007.07
    -
    2010.12

  • 2004年度統計関連学会連合大会　事務局, 

    2003.10
    -
    2004.09

  • 2003年度統計関連学会連合大会　事務局, 

    2002.10
    -
    2003.09

  • Fourth International Sysmposium on Independent Component Analysis and blind source Separation (ICA2003), 

    2002.04
    -
    2003.04

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Committee Experiences 【 Display / hide 】

  • 2024.05
    -
    2026.04

    Director, Japanese Society of Applied Statistics

  • 2024.05
    -
    2026.04

    Director, Japanese Federation of Statistical Science Associations

  • 2023.04
    -
    Present

    Coordinating Editor, Japanese Journal of Statistics and Data Science

  • 2022.04
    -
    Present

    Chair, Special Committee for the Promotion of Diversity, Japan Statistical Society

  • 2021.09
    -
    2023.09

    委員長, 日本統計学会学会活動特別委員会

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