Minami, Mihoko

写真a

Affiliation

Faculty of Science and Technology (Mita)

Position

Professor Emeritus

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 】

  • Bias-Adjusting Observer Species Composition Estimates of Tuna Caught by Purse-Seiners Using Port-Sampling Data: A Mixed-Effects Modeling Approach Based on Paired Well-Level Data

    Cleridy E Lennert-Cody, Cristina De La Cadena, Luis Chompoy, Mark N Maunder, Daniel W Fuller, Ernesto Altamirano Nieto, Mihoko Minami, Alexandre Aires-da-Silva

    Fishes (MDPI)  10 ( 10 ) 494 2025.10

    Research paper (scientific journal), Accepted

  • Peripubertal lung growth pattern in Japanese school children

    Konno S., Taguri M., Odajima H., Minami M., Takebayashi T., Nitta H., Nishimura M.

    Physiological Reports 13 ( 16 )  2025.08

    　View Summary

    The peripubertal growth pattern of lung function remains underexplored in relation to height growth. This study aimed to first clarify the relationship between the age at peak growth velocity in lung function variables and the age at peak height velocity (APHV) and second identify sex differences in lung function growth patterns. Lung function and height were measured annually in children aged 9–15 years (elementary schools, N = 1307; junior high schools, N = 792) from 2011 to 2018. Children were categorized quarterly according to APHV, using the Super Imposition by Translation and Rotation model. The age at peak growth velocity for forced vital capacity (FVC) and forced expiratory volume in 1 s (FEV<inf>1</inf>) lagged behind APHV by 2–12 months. The later the APHV, the greater the numerical lag, although this was not significant. In males, but not females, the trajectory of FEV<inf>1</inf>/FVC values gradually decreased to reach the lowest levels and then gradually increased with age (U-shaped curve) in all quartiles. Both FVC and FEV<inf>1</inf> overwhelmed in males compared with those in females when the height exceeded 150–160 cm. Our results highlight significant variability in peripubertal lung growth with height and sex-related differences in the growth of airways and parenchymal components.

  • All-Cause and Cause-Specific Mortality Associated with Long-Term Exposure to Fine Particulate Matter in Japan: The Ibaraki Prefectural Health Study

    Michikawa T., Nishiwaki Y., Asakura K., Okamura T., Takebayashi T., Hasegawa S., Milojevic A., Minami M., Taguri M., Takeuchi A., Ueda K., Sairenchi T., Yamagishi K., Iso H., Irie F., Nitta H.

    Journal of Atherosclerosis and Thrombosis 32 ( 8 ) 982 - 993 2025

    ISSN  13403478

    　View Summary

    Aims: Long-term exposure to fine particulate matter (PM2.5) is causally associated with mortality and cardiovascular disease. However, in terms of cardiovascular cause-specific outcomes, there are fewer studies about stroke than about coronary heart disease, particularly in Asia. Furthermore, there remains uncertainty regarding the PM2.5-respiratory disease association. We examined whether long-term exposure to PM2.5 is associated with all-cause, cardiovascular and respiratory disease mortality in Japan. Methods: We used data of 46,974 participants (19,707 men; 27,267 women), who were enrolled in 2009 and followed up until 2019, in a community-based prospective cohort study (the second cohort of the Ibaraki Prefectural Health Study). We estimated PM2.5 concentrations using the inverse distance weighing methods based on ambient air monitoring data, and assigned each participant to administrative area level concentrations. A Cox proportional hazard model was applied to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) of mortality. Results: During the average follow-up of 10 years, we confirmed 2,789 all-cause deaths. All outcomes including stroke mortality did not significantly increase as the PM2.5 concentration increased. For non-malignant respiratory disease mortality, the multivariable adjusted HR per 1 µg/m<sup>3</sup> increase in the PM2.5 concentration was 1.09 (95% CI = 0.97–1.23). Conclusions: In this population exposed to PM2.5 at concentrations of 8.3–13.1 µg/m<sup>3</sup>, there was no evidence that long-term exposure to PM2.5 had adverse effects on mortality. Weak evidence of positive association observed for non-malignant respiratory disease mortality needs further studies in other populations.

  • 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

    　View Summary

    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.

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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

  • 統計輪講

    Keio University

    2014.04
    -
    2015.03
    , 

    Autumn Semester, Within own ｆaculty, 1h

  • データ解析同演習

    Keio University

    2014.04
    -
    2015.03
    , 

    Autumn Semester, Seminar, 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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