Updated on 2022/12/21

写真a

 
SAKAMOTO Wataru
 
Organization
Faculty of Environmental and Life Science Professor
Position
Professor
External link

Degree

  • Ph.D in Engineering ( 1998.3   Osaka University )

Research Interests

  • Statistical Science

Research Areas

  • Informatics / Statistical science  / 計算統計学,正則化法,ベイズ統計

Education

  • Osaka University   大学院基礎工学研究科   数理系専攻 博士後期課程

    1995.4 - 1998.3

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    Country: Japan

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  • Osaka University   大学院基礎工学研究科   数理系専攻 博士前期課程

    1993.4 - 1995.3

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  • Kyoto University   理学部  

    1989.4 - 1993.3

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    Notes: 主として数学を専攻

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Research History

  • Okayama University   Division of Human Ecology, Graduate School of Environmental and Life Science   Professor

    2013.4

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    Country:Japan

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  • Osaka University   Graduate School of Engineering Science Department of Systems Innovation Division of Mathematical Science   Associate Professor

    2007.4 - 2013.3

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    Country:Japan

    Notes:職名変更

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  • Osaka University   Graduate School of Engineering Science Department of Systems Innovation Division of Mathematical Science   Associate Professor (as old post name)

    2003.10 - 2007.3

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  • Osaka University   Graduate School of Engineering Science   Research Assistant

    1998.4 - 2003.9

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    Country:Japan

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Professional Memberships

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Committee Memberships

  • 日本統計学会   代議員  

    2021.4   

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    Committee type:Academic society

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  • 情報・システム研究機構・統計数理研究所   統計思考院運営委員会  

    2017.2 - 2019.3   

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    Committee type:Other

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  • The Asian Regional Section of the International Association for Statistical Computing   Board of Directors member  

    2016.1 - 2019.12   

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    Committee type:Academic society

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  • International Association for Statistical Computing   Scientific Secretary  

    2009.8 - 2011.8   

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  • 日本計算機統計学会   評議員・理事  

    2001.1   

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    Committee type:Academic society

    評議員 (2003-2016, 2018-2020)
    庶務理事 (2001-2006)
    企画理事 (2007-2010)
    渉外理事 (2013-2014)
    欧文誌編集理事 (2015-2018)
    国際交流理事 (2019-2020)

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  • 日本計量生物学会   評議員  

    2013.1 - 2018.12   

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    Committee type:Academic society

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  • 日本統計学会   理事(広報)  

    2006.9 - 2008.8   

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  • 特定非営利活動法人 医学統計研究会   理事  

    2006.4 - 2021.3   

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    Committee type:Other

    医学統計研究会

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Papers

  • Bias‐reduced marginal Akaike information criteria based on a Monte Carlo method for linear mixed‐effects models Reviewed International journal

    Wataru Sakamoto

    Scandinavian Journal of Statistics   46 ( 1 )   87 - 115   2019.3

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    Authorship:Lead author   Language:English   Publishing type:Research paper (scientific journal)   Publisher:Wiley  

    DOI: 10.1111/sjos.12339

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    Other Link: https://onlinelibrary.wiley.com/doi/full-xml/10.1111/sjos.12339

  • Statistical Issues on Japanese Criteria of Metabolic Syndrome Reviewed

    SAKAMOTO Wataru, ISOGAWA Naoki, GOTO Masashi

    Kodo Keiryogaku   35 ( 2 )   177 - 192   2008

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    Authorship:Lead author   Language:Japanese   Publishing type:Research paper (scientific journal)   Publisher:The Behaviormetric Society of Japan  

    The article by The Examination Committee of Criteria for 'Obesity Disease' in Japan and Japan Society for the Study of Obesity (2002) established cut-off points of waist circumference at 85 cm for males and 90 cm for females as criteria of obesity disease. Their article has also become a basis for criteria of metabolic syndrome in Japan; however, their article has various problems on statistical aspects. How the criteria on waist circumference should vary was investigated through reexamination based on descriptions in their article. First, their article obtained criteria on visceral fat area (VFA) from data in which males and females were pooled, while our reexamination suggested that we should use separate criteria on VFA between males and females. Second, their article inappropriately used regression lines to estimate waist circumference corresponding to VFA cut-off points. Our reexamination with errors-in-variables models suggested alternative cut-off points of waist circumference at 87 cm for males and 85 cm for females. Our simulation confirmed that the criteria by their article might lead to inappropriate diagnosis which is strict for males and easy for females.

    DOI: 10.2333/jbhmk.35.177

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  • 罰則付きスプラインによる非線形回帰構造の推測 Reviewed

    坂本 亘, 井筒理人, 白旗慎吾

    計算機統計学   21 ( 1-2 )   55 - 94   2008

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  • EXTRACTING NON-LINEAR ADDITIVE REGRESSION STRUCTURE WITH POWER-ADDITIVE SMOOTHING SPLINES Reviewed

    Sakamoto Wataru

    Journal of the Japanese Society of Computational Statistics   20 ( 1 )   83 - 108   2007

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    Authorship:Lead author   Language:English   Publishing type:Research paper (scientific journal)   Publisher:Japanese Society of Computational Statistics  

    The additive regression model assumes additivity among explanatory variables and other rigid requirements, which might give poor estimation of regression functions. Transforming response variables is a useful method to diagnose additivity and other requirements. From a practical point of view, parametric transformations such as the Box-Cox power transformation would give more helpful suggestions in interpreting results of analysis than nonparametric transformations. The power additive smoothing spline (PASS) model is proposed to diagnose the validity of assuming additivity in the additive regression model. The smooth functions (and often regression parameters) are estimated with a penalized likelihood approach, and the power and the smoothing parameters, which govern global nonlinear regression structure, are estimated with the empirical Bayes method, in which a Laplace approximation of the marginal likelihood is developed. The PASS model is applied to some data sets, and also its performance is examined through a simulation experiment. It is shown that the PASS model can extract an appropriate regression structure if true structure is additive after a Box-Cox power transformation of responses.

    DOI: 10.5183/jjscs1988.20.83

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  • MARS: selecting basis functions and knots with an empirical Bayes method Reviewed

    Wataru Sakamoto

    COMPUTATIONAL STATISTICS   22 ( 4 )   583 - 597   2007

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    Authorship:Lead author   Language:English   Publishing type:Research paper (scientific journal)   Publisher:SPRINGER HEIDELBERG  

    An empirical Bayes method to select basis functions and knots in multivariate adaptive regression spline (MARS) is proposed, which takes both advantages of frequentist model selection approaches and Bayesian approaches. A penalized likelihood is maximized to estimate regression coefficients for selected basis functions, and an approximated marginal likelihood is maximized to select knots and variables involved in basis functions. Moreover, the Akaike Bayes information criterion (ABIC) is used to determine the number of basis functions. It is shown that the proposed method gives estimation of regression structure that is relatively parsimonious and more stable for some example data sets.

    DOI: 10.1007/s00180-007-0075-7

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  • Diagnosing Homoscedasticity with the Power-weighted Smoothing Spline Reviewed

    SAKAMOTO Wataru

    Ouyou toukeigaku   33 ( 1 )   27 - 49   2004

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    Authorship:Lead author   Language:Japanese   Publishing type:Research paper (scientific journal)   Publisher:Japanese Society of Applied Statistics  

    In nonparametric regression models, the requirements of homoscedasticity and so on are implicitly assumed, which leads to poor estimation of regression functions. A power weighted smoothing spline (PWSS) model, whose objective is to diagnose homoscedasticity as well as to estimate unknown nonlinear regression structure, is assumed. The responses in an additive regression model are power-transformed, and then their variances after transformation are assumed to be constant. Smoothing splines are obtained as estimated functions by maximizing the penalized likelihood, and a reweighted version of a backfitting algorithm is constructed. A power-transformation parameter and smoothing parameters, which control smoothness of the functions, are estimated by maximizing the marginal likelihood, based on Bayesian approaches to smoothing splines. A form of the marginal likelihood, which yields comparatively easy computation, is derived using the property that smoothing splines are the best linear unbiased predictor of a linear mixed model. Examination of some data sets from the literature and a simulation experiment show that the power transformation estimated with the PWSS model attains homoscedasticity while taking nonlinear structure into account.

    DOI: 10.5023/jappstat.33.27

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    Other Link: https://jlc.jst.go.jp/DN/JALC/00242632045?from=CiNii

  • 制限付き最尤推定法による平滑化パラメータの選定:効率的な計算方式とその適用 Reviewed

    坂本 亘

    計算機統計学   15 ( 1 )   19 - 45   2002

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  • Likelihood-based cross-validation score for selecting the smoothing parameter in maximum penalized likelihood estimation Reviewed

    W Sakamoto, S Shirahata

    COMMUNICATIONS IN STATISTICS-THEORY AND METHODS   28 ( 7 )   1671 - 1698   1999

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    Authorship:Lead author   Language:English   Publishing type:Research paper (scientific journal)   Publisher:MARCEL DEKKER INC  

    Maximum penalized likelihood estimation is applied in non(semi)-parametric regression problems, and enables us exploratory identification and diagnostics of nonlinear regression relationships. The smoothing parameter lambda controls trade-off between the smoothness and the goodness-of-fit oof a function. The method of cross-validation is used for selecting lambda, but the generalized cross-validation, which is based on the squared error criterion, shows bad behavior in non-normal distribution and can not often select reasonable lambda. The purpose of this study is to propose a method which gives more suitable lambda and to evaluate the performance of it.
    A method of simple calculation for the delete-one estimates in the likelihood-based cross-validation (LCV) score is described. A score of similar form to the Akaike information criterion (AIC) is also derived. The proposed scores are compared with the ones of standard procedures by using data sets in literatures. Simulations are performed to compare the patterns of selecting lambda and overall goodness-of-fit and to evaluate the effects of some factors.
    The LCV scares by the simple calculation provide good approximations to the exact one if lambda is not extremely small. Furthermore the LCV scores by the simple calculation have little risk of choosing extremely small lambda and make it possible to select lambda adaptively. They have the effect of reducing the bias of estimates and provide better performance in the sense of overall goodness of-fit. These scores are useful especially in the case of small sample size and in the case of binary logistic regression.

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  • SIMPLE CALCULATION OF LIKELIHOOD-BASED CROSS-VALIDATION SCORE IN MAXIMUM PENALIZED LIKELIHOOD ESTIMATION OF REGRESSION FUNCTIONS Reviewed

    Sakamoto Wataru, Shirahata Shingo

    Journal of the Japanese Society of Computational Statistics   10 ( 1 )   27 - 40   1997

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    Authorship:Lead author   Language:Japanese   Publishing type:Research paper (scientific journal)   Publisher:Japanese Society of Computational Statistics  

    In maximum penalized likelihood estimation, approaches of cross-validation (CV) are often useful in selecting a smoothing parameter. The CV score based on squared-error criterion behaves more badly than the likelihood-based score. However, it is expensive to calculate the likelihood-based score. Hence we propose a method for simple calculation of this score. The simple calculation is derived as an analogue of the deletion lemma in ordinary or penalized least squares, and is shown to be related to the one-step approximation to the estimates of parameters for the Newton-Raphson method. Our method is applied to binary data from some case studies in the context of logistic regression. It is illustrated that the simple calculation method well behaves and gives a good approximation to the likelihood-based score calculated by the delete-one method,

    DOI: 10.5183/jjscs1988.10.27

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  • セミパラメトリック回帰問題におけるスプライン平滑化 Reviewed

    坂本 亘, 白旗慎吾

    計算機統計学   9 ( 1 )   13 - 35   1996

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  • Optimum Tuning Parameter Selection in Generalized lasso for Clustering with Spatially Varying Coefficient Models Reviewed

    Septian Rahardiantoro, Wataru Sakamoto

    IOP Conference Series: Earth and Environmental Science   950 ( 1 )   012093 - 012093   2022.1

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    Language:English   Publishing type:Research paper (international conference proceedings)   Publisher:IOP Publishing  

    Abstract

    Spatial clustering with spatially varying coefficient models is useful for determining the region with common effects of variables in spatial data. This study focuses on selecting the optimum tuning parameter of the generalized lasso for clustering with the spatially varying coefficient model. The k-fold cross-validation (CV) may fail to split spatial data into a training set and a testing set, if a region contains only a few observations. Moreover, the k-fold CV is known to give a biased estimate of the out-of-sample prediction error. Therefore, we investigated the performance of approximate leave-one-out cross-validation (ALOCV) in comparison with k-fold CV for selecting the tuning parameter in a simulation study on 2-dimensional grid. The ALOCV yielded smaller error than k-fold CV and could detect edges with differences shrunk by generalized lasso appropriately. Then, the ALOCV for selecting the optimum tuning parameter of the generalized lasso in fitting the spatially varying coefficient model is applied to the Chicago crime data. The result of selection by ALOCV was in accordance with the conclusion suggested in the preceding literature. Clustering into regions in advance for making k-fold CV feasible may lead to a wrong result of clustering with a spatially varying coefficient model.

    DOI: 10.1088/1755-1315/950/1/012093

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    Other Link: https://iopscience.iop.org/article/10.1088/1755-1315/950/1/012093/pdf

  • Multiple mutations in RNA polymerase β-subunit gene (rpoB) in Streptomyces incarnatus NRRL8089 enhance production of antiviral antibiotic sinefungin: modeling rif cluster region by density functional theory Reviewed International journal

    Saori Ogawa, Hitomi Shimidzu, Koji Fukuda, Naoki Tsunekawa, Toshiyuki Hirano, Fumitoshi Sato, Kei Yura, Tomohisa Hasunuma, Kozo Ochi, Michio Yamamoto, Wataru Sakamoto, Kentaro Hashimoto, Hiroyuki Ogata, Tadayoshi Kanao, Michiko Nemoto, Kenji Inagaki, Takashi Tamura

    Bioscience, Biotechnology, and Biochemistry   85 ( 5 )   1275 - 1282   2021.4

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    Language:English   Publishing type:Research paper (scientific journal)   Publisher:Oxford University Press (OUP)  

    <title>ABSTRACT</title>
    Streptomyces incarnatus NRRL8089 produces the antiviral, antifungal, antiprotozoal nucleoside antibiotic sinefungin. To enhance sinefungin production, multiple mutations were introduced to the rpoB gene encoding RNA polymerase (RNAP) β-subunit at the target residues, D447, S453, H457, and R460. Sparse regression analysis using elastic-net lasso-ridge penalties on previously reported H457X mutations identified a numeric parameter set, which suggested that H457R/Y/F may cause production enhancement. H457R/R460C mutation successfully enhanced the sinefungin production by 3-fold, while other groups of mutations, such as D447G/R460C or D447G/H457Y, made moderate or even negative effects. To identify why the rif cluster residues have diverse effects on sinefungin production, an RNAP/DNA/mRNA complex model was constructed by homology modeling and molecular dynamics simulation. The 4 residues were located near the mRNA strand. Density functional theory–based calculation suggested that D447, H457, and R460 are in direct contact with ribonucleotide, and partially positive charges are induced by negatively charged chain of mRNA.

    DOI: 10.1093/bbb/zbab011

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    Other Link: http://academic.oup.com/bbb/article-pdf/85/5/1275/37329090/zbab011.pdf

  • Clustering Regions Based on Socio-Economic Factors Which Affected the Number of COVID-19 Cases in Java Island Reviewed

    Septian Rahardiantoro, Wataru Sakamoto

    Journal of Physics: Conference Series   1863 ( 1 )   012014 - 012014   2021.3

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    Authorship:Last author   Language:English   Publishing type:Research paper (international conference proceedings)   Publisher:IOP Publishing  

    DOI: 10.1088/1742-6596/1863/1/012014

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  • Microsimulation model for evaluating the effect of cancer control program: example for colorectal cancer Reviewed

    加茂憲一, 福井敬祐, 福井敬祐, 坂本亘, 伊藤ゆり

    計量生物学   41 ( 2 )   93 - 115   2021

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  • Meta-analysis of a continuous outcome combining individual patient data and aggregate data: a method based on simulated individual patient data Reviewed

    Yusuke Yamaguchi, Wataru Sakamoto, Masashi Goto, Jan A. Staessen, Jiguang Wang, Francois Gueyffier, Richard D. Riley

    Research Synthesis Methods   5 ( 4 )   322 - 351   2014.12

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    DOI: 10.1002/jrsm.1119

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  • REGRESSION ANALYSIS USING LASSO RANDOM FOREST Reviewed

    Nakamura Masatoshi, Shimokawa Toshio, sakamoto Wataru, Goto Masashi

    Bulletin of the Computational Statistics of Japan   26 ( 1 )   17 - 31   2013

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    Language:Japanese   Publishing type:Research paper (scientific journal)   Publisher:Japanese Society of Computational Statistics  

    Random Forest (RF) is one of tree-structured approaches, such as classification and regression trees, involved in ensemble learning method to predict outcome more precisely. In this paper we proposed an adjusted RF based on lasso (lasso-RF) for further predictive performance in regression analysis. In particular, we integrated lasso which use one of shrinkage estimators into the tree-structured model of RF. Practically we carried out two case studies and a small scale simulation with factors which influence prediction of outcome such as sample size {100, 200, 400}, methods {RF, lasso-RF} and bootstrap re-sampling times {100, 200, 400}, and evaluated predictive performance. Our case studies suggested that lasso-RF decreased the mean squared errors between true and estimate outcomes less than original RF. By our simulation study, we evaluated influencing factors on the mean squared errors based on analysis of variance with above three factors and their interaction, and showed that the factor of methods as fixed effect was significant at level 0.05 with proportion of variation 29.2%.

    DOI: 10.20551/jscswabun.26.1_17

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  • An evaluation of treatment-covariate interaction in meta-analysis with marginalizing the missing individual patient data Reviewed

    Yamaguchi Y, Sakamoto W, Shirahata S, Goto M

    26 ( 1 )   1 - 16   2013

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    DOI: 10.5183/jjscs.1212001_203

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  • PERFORMANCE EVALUATION OF BAYESIAN MODEL DIAGNOSTIC METHODS THAT FOCUS ON PREDICTION Reviewed

    Isogawa Naoki, Sakamoto Wataru, Goto Masashi

    Bulletin of the Computational Statistics of Japan   25 ( 1 )   1 - 13   2012

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    Language:Japanese   Publishing type:Research paper (scientific journal)   Publisher:Japanese Society of Computational Statistics  

    In the framework of Bayesian approach, though we can select various prior distributions according to the situations, many models which should be evaluated exist. However, we think that the diagnosis methods for these models have proper diagnostic situations. In this paper, we consider two diagnostic methods that focus on prediction: the Bayesian predictive information criterion (BPIC) and the prior and posterior predictive checking approach (PCA) and conduct some simulations for the purpose of clarifying the feature of these methods and suggesting the effective diagnostic situations. As the results, regardless of whether the prior mean was true or not, BPIC showed the low values on the occasion with strong prior information, but PCA showed the high predictive checking probabilities on the occasion with weak prior information. So, it might happen that the model with non-true prior mean was selected by simulation setting. To select a proper model, it is necessary to find the performance of the model diagnoses in the situation before model evaluations.

    DOI: 10.20551/jscswabun.25.1_1

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  • 「保健指導」に関する評価の試み Reviewed

    五十川直樹, 池邉淑子, 坂本 亘, 後藤昌司

    行動計量学   38 ( 1 )   51 - 63   2011

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  • Relationship between Transmission Intensity and Incidence of Dengue Hemorrhagic Fever in Thailand Reviewed

    Suwich Thammapalo, Yoshiro Nagao, Wataru Sakamoto, Seeviga Saengtharatip, Masaaki Tsujitani, Yasuhide Nakamura, Paul G. Coleman, Clive Davies

    PLoS Neglected Tropical Diseases   2 ( 7 )   e263 - e263   2008.7

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    DOI: 10.1371/journal.pntd.0000263

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  • 関数データの判別分析―線形的手法と関数部分空間法 Reviewed

    Dou Xiaoling, 白旗慎吾, 坂本 亘

    計算機統計学   19 ( 1 )   13 - 30   2006

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  • AICによるウェーブレット基底関数の選択 Reviewed

    松嶋優貴, 白旗慎吾, 坂本 亘

    応用統計学   33 ( 2 )   201 - 219   2004

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  • Statistical evaluation of umbrella dose-response relationships Reviewed

    Baba M, Fujisawa M, Sakamoto W, Goto M

    Journal of the Japanese Society of Computational Statistics   15 ( 2 )   281 - 294   2003

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    In the drug development process, it is essential to assess the relationship (mechanism) between dose and response of a biological system following drug administration. This mechanism is known as the "dose-response relationship". Then, dose-response relationships are often evaluated based on a monotonic hypothesis. However, in practice, we may often encounter non-monotonic dose-response relationships, such as the umbrella relationship, which then makes interpretation of the relationships somewhat problematic. In this paper, to assess such umbrella dose-response relationships, the cumulative dose logit model is proposed and applied to an example. To evaluate properties of this model, some Monte-Carlo studies are performed. The results of the cumulative dose logit model are compared with those of the quadratic logit model. This indicates that the cumulative dose logit model provides more stable estimates than the quadratic logit model in estimating the maximum effective dose. It is suggested that the cumulative dose logit model is appropriate for assessing non-monotonic dose-response relationships.

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  • CONSTRUCTION OF CONFIDENCE BANDS FOR SIMPLE LINEAR REGRESSION OVER BOUNDED INTERVALS Reviewed

    Fujisawa Masaki, Baba Mitsumasa, Sakamoto Wataru, Goto Masashi

    Bulletin of the Computational Statistics of Japan   14 ( 1 )   29 - 44   2002

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    Language:Japanese   Publishing type:Research paper (scientific journal)   Publisher:Japanese Society of Computational Statistics  

    In this article, we discuss methods to construct confidence bands for regression function, especially to construct uniform width-confidence bands and trapezoidal confidence bands in a finite interval of interest. With case studies and numerical experiments, we examined the coverage probability and the efficiency (area ratio) of these confidence bands. From a viewpoint of the coverage probability, it is reasonable to use the uniform width- confidence bands (Gafarian bands) and trapezoidal confidence bands (Bowden-Graybill bands) for the finite interval of interest, rather than to use the Working-Hotelling bands. In particular, the use of these confidence bands can be emphasized when a length of the interval of interest is smaller than that of the observation interval. Moreover, from the examination about efficiency, it is strongly recommended to use the trapezoidal confidence bands when the interval of interest is near the relative ends of the observation interval.

    DOI: 10.20551/jscswabun.14.1_29

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  • Likelihood Ratio Test in the Two Phase Linear Regression Problem. Reviewed

    BIAN Qi, SAKAMOTO Wataru, SHIRAHATA Shingo

    Ouyou toukeigaku   26 ( 3 )   135 - 150   1997

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    Language:Japanese   Publishing type:Research paper (scientific journal)   Publisher:Japanese Society of Applied Statistics  

    DOI: 10.5023/jappstat.26.135

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Books

  • 統計学辞典

    白旗慎吾, 内田雅之, 熊谷悦生, 黒木 学, 阪本雄二, 坂本 亘

    共立出版  2010.10  ( ISBN:9784320019393

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    Total pages:vi, 512p   Language:Japanese

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MISC

  • Inference on variance components near boundary in linear mixed effect models Reviewed

    Wataru Sakamoto

    Wiley Interdisciplinary Reviews: Computational Statistics   11 ( 6 )   2019.11

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    Language:English   Publishing type:Article, review, commentary, editorial, etc. (scientific journal)   Publisher:Wiley  

    DOI: 10.1002/wics.1466

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    Other Link: https://onlinelibrary.wiley.com/doi/full-xml/10.1002/wics.1466

  • p 値と仮説検定:どう教えればよいか

    坂本 亘

    2017年度統計関連学会連合大会 講演報告集   2017.9

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  • Cluster detection of disease mapping data based on latent Gaussian Markov random field models Reviewed

    Sakamoto, W

    Proceedings of COMPSTAT 2016: 22th International Conference on Computational Statistics (Oviedo, Spain)   267 - 277   2016.8

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  • 医学統計概論―多変量解析とは―

    坂本 亘, 後藤昌司

    日本心血管インターベンション治療学会誌   2 ( 4 )   295 - 302   2010

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  • MARS: selecting basis and knots with the empirical Bayes method Reviewed

    Sakamoto, W

    Compstat 2006: Proceedings in Computational Statistics (CD-ROM) (Rome, Italy)   1397 - 1404   2006.8

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  • 計算機統計学に関連する国内外の学会・雑誌の動向

    坂本 亘

    エストレーラ   ( 328 )   2021.7

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  • 数理モデルによる大腸腫瘍発生過程のシミュレーション

    坂本 亘, 伊森晋平, 加茂憲一

    厚労科研総括・分担報告書   22 - 25   2017.3

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  • An analysis of Japanese liver cancer mortality data with Bayesian age-period-cohort models

    Proceedings of the International Conference for JSCS 30th Anniversary in Seattle   2016.10

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  • 数理モデルによる腫瘍発生過程のシミュレーション

    坂本 亘, 伊森晋平, 加茂憲一

    厚労科研総括・分担研究報告書   26 - 30   2016.3

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  • がん対策推進基本計画の効果検証と目標設定に関する研究 肝臓がん自然史モデルに対する数理モデルと推定アルゴリズムについて

    伊森晋平, 田中純子, 加茂憲一, 坂本亘, 伊藤ゆり, 福井敬祐

    がん対策推進基本計画の効果検証と目標設定に関する研究 平成27年度 総括・分担研究報告書   2016

  • 数理モデルによる腫瘍発生のマイクロシミュレーション

    坂本 亘, 伊藤ゆり, 加茂憲一

    厚労科研総括・分担研究報告書   32 - 35   2015.3

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  • メタ・アナリシスの要点と解釈

    山口祐介, 坂本亘, 後藤昌司

    骨粗鬆症治療   14 ( 3 )   264 - 267   2015

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    Other Link: http://search.jamas.or.jp/link/ui/2016087928

  • 一般化線形混合モデルに対するINLAによるBayes流推測の性能評価

    萩原 駿祐, 坂本 亘

    日本計算機統計学会第27回シンポジウム論文集   27   131 - 134   2013.11

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    DOI: 10.20551/jscssymo.27.0_131

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  • A meta-analysis method based on simulated individual patient data Reviewed

    Yamaguchi Y, Sakamoto W, Shirahata S, Goto M

    The 58th World Statistical Congress of the International Statistical Institute (ISI 2011): Proceedings (USB media, 6 pages). (Dublin, Ireland)   2011.8

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  • Failure prediction method for network management system by using Bayesian network and shared database Reviewed

    Erwin Harahap, Wataru Sakamoto, Hiroaki Nishi

    8th Asia-Pacific Symposium on Information and Telecommunication Technologies, APSITT 2010   2010.6

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    Network Management System (NMS) is a service that employs a variety of tools, applications, and devices to assist network administrators on monitoring and maintaining network. Keeping the network in high quality of service is the main purpose of NMS. This paper proposed a method to solve the network problem by making a prediction of failure based on network-data behavior. The prediction represented by conditional probability generated by Bayesian network. Bayesian network is a probability graphical model for representing the probabilistic relationship among a large number of variables and doing probabilistic inference with those variables. In order to describe how the prediction works, we discuss the prediction result by simulation on network congestion.

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  • データ復元・拡大問題における計算統計の展望

    坂本 亘

    日本計算機統計学会・第24回シンポジウム講演論文集   105 - 108   2010

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  • Selecting an appropriate transformation of responses for fitting a linear or additive mixed model Reviewed

    Sakamoto, W

    Proceedings of the 57th Session of the International Statistical Institute (ISI 2009) (2 pages). Durban, South Africa)   2009.8

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  • Selecting an appropriate transformation of responses for fitting a semiparametric mixed model Reviewed

    Sakamoto, W

    IASC2008: Joint Meeting of 4th World Conference of the IASC and 6th Conference of the Asian Regional Section of the IASC on Computational Statistics & Data Analysis: Proceedings (CD-ROM: 6 pages). (Yokohama, Japan)   2008.12

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  • A simulation study on evaluating contribution of variables with empirical Bayes MARS Reviewed

    Sakamoto, W

    Bulletin of the International Statistical Institute 56th Session: Proceedings (CD-ROM: 4 pages). (Lisbon, Portugal)   2007.8

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  • 経験Bayes法による多変量適応的回帰スプラインの基底および節点の選定

    坂本 亘

    日本計算機統計学会第20回大会講演論文集   20   165 - 168   2006

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    DOI: 10.20551/jscstaikai.20.0_165

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  • MARS: selecting basis and knots with the empirical Bayes method Reviewed

    Sakamoto, W

    Proceedings of the 5th IASC Asian Conference on Statistical Computing (Hong Kong)   135 - 138   2005.12

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  • Diagnosing non-linear regression structure with power additive smoothing splines

    Sakamoto, W

    Proceedings of the ISM/KIER Joint Conference on Nonparametric and Semiparametric Statistics (Tokyo, Japan)   249 - 262   2005.3

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  • 近似最大周辺尤度法:平滑化スプライン・モデルへの応用

    坂本 亘

    「ノンパラメトリック・セミパラメトリック法を用いた統計解析理論とその学際的応用」研究報告書   217 - 229   2004

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  • Exploring nonlinear structure with nonparametric regression Reviewed

    Sakamoto, W

    Proceedings of the 54th Conference of the International Statistical Institute (CD-ROM) (Berlin, Germany)   2003.8

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  • Approximation of maximum marginal likelihood in non-Gaussian nonparametric regression models Reviewed

    Sakamoto, W

    Proceedings of the 4th ARS Conference of the IASC (Busan, Korea)   22 - 25   2002

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  • 制限付き最尤推定法による平滑化パラメータの選定:シミュレーションによる評価

    坂本 亘

    日本計算機統計学会第16回シンポジウム講演論文集   16   101 - 104   2002

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    DOI: 10.20551/jscssymo.16.0_101

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  • Statistical evaluation of umbrella dose-response relationships Reviewed

    Baba M, Sakamoto W, Goto M

    Proceedings of the 4th ARS Conference of the IASC (Busan, Korea)   185 - 186   2002

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  • 制限付き最尤推定法による平滑化パラメータの選定

    坂本 亘

    統計数理研究所・共同研究リポート, No. 143, pp. 51-68.   143   51 - 68   2001

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  • 制約付き最尤推定法による平滑化パラメータの選定

    坂本 亘

    第68回日本統計学会講演報告集   217 - 218   2000

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  • 平滑化スプラインによる非線形構造の探索

    坂本 亘

    統計数理研究所・共同研究リポート   134   27 - 42   2000

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  • 平滑化パラメータの選定における制約付き最尤推定法の適用とその評価

    坂本 亘

    第67回日本統計学会講演報告集   404 - 405   1999

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  • 平滑化パラメータの選定における制約付き最尤推定法の適用とその評価

    坂本 亘

    統計数理研究所・共同研究リポート   118   109 - 118   1999

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  • ノンパラメトリック回帰における平滑化パラメータの制約付き最尤推定

    坂本 亘

    日本計算機統計学会第12回シンポジウム講演論文集   12   49 - 52   1998

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    DOI: 10.20551/jscssymo.12.0_49

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  • Simple calculation of likelihood-based cross-validation score in maximum penalized likelihood estimation Reviewed

    Sakamoto W, Shirahata S

    Multivariate Analysis and Computing: Proceedings of the Ninth Korea and Japan Joint Conference of Statistics (KJCS-97), (Jeju-do, Korea)   267 - 272   1997.12

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  • 罰則付き最尤推定における尤度に基づく交差確認得点の簡便計算

    坂本 亘, 白旗慎吾

    日本計算機統計学会第10回シンポジウム講演論文集   10   20 - 23   1996

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    DOI: 10.20551/jscssymo.10.0_20

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Presentations

  • Analysis of disease mapping data: how to detect clusters of higher prevalence more flexibly Invited

    Sakamoto, W

    ICMSDS 2020 (Bogor, Indonesia: Online)  2020.11.11 

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  • Analysis of Spatial Data with a Gaussian Mixture Markov Random Field Model

    Sakamoto, W

    IASC-ARS/NZSA 2017  2017.12 

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  • Spatially varying coefficient modeling of numerical and categorical predictor variables in the generalized lasso

    Septian Rahardiantoro, Wataru Sakamoto

    IASC-ARS2022  2022.2.23 

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  • Bayes 階層モデリングによる疾病地図解析

    坂本 亘

    大分統計談話会第60回大会  2019.10 

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  • 時空間従属構造を考慮した高リスク集積領域の同定

    坂本 亘

    大分統計談話会第58回大会  2018.10 

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  • R Shiny implementation of lasagna plot: interactive manipulation and visualization of longitudinal data

    Sakamoto W, Kaneda M

    IFCS 2017  2017.8 

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  • ベイズ統計:結果から原因を推測する

    坂本 亘

    岡山大学公開講座2017「身近に広がる数学II」  2017.7.30 

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  • Cluster detection of disease mapping data based on latent Gaussian Markov random field models Invited

    Wataru Sakamoto

    2016 IASC-ARS Conference (Daejeon, Korea)  2016.11 

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  • Environmental and medical applications of latent Gaussian Markov random field models Invited

    Sakamoto, W.

    Utah State University Math and Stat Special Research Colloquium  2016.10 

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  • 統計的推測の基本

    坂本 亘

    医学統計研究会・定例シンポジウム2016 「医療で必要とされる統計的基礎知識」  2016.10 

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  • R による計量データの解析:パッケージと開発環境

    坂本 亘

    医学統計研究会特定主題セミナー「臨床評価における計算環境Rとその課題」  2015.11 

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  • Performance of Bayes inference with integrated nested Laplace approximation in generalized linear mixed effect models

    Hagihara, S, Sakamoto, W

    The 24th South Taiwan Statistics Conference and 2015 Chinese Institute of Probability and Statistics Annual Meeting (STSC/CIPS2015)  2015.6 

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  • Information criteria for linear mixed effect models: bias correction based on a Monte Carlo method

    2014.5 

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  • 潜在構造を伴う統計モデルの推測と その複雑さの制御 Invited

    坂本 亘

    広島大学統計科学研究拠点セミナー  2014 

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  • 医学統計の実践で起こる過誤~科学論文に見られる誤用から学ぶ~

    坂本 亘

    医学統計研究会・定例シンポジウム2012 「医療に必要とされる統計的基礎知識」  2012.10 

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  • Selecting an optimal mixed effect model based on information criteria Invited

    ISBIS 2012 (Bangkok, Thailand)  2012.6 

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  • Selecting variance structure in mixed effect models by information criteria based on Monte Carlo approximations

    Sakamoto, W

    Joint Meeting of the 2011 Taipei International Statistical Symposium and 7th Conference of the Asian Regional Section of the IASC  2011.12 

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  • Selecting an optimal mixed effect model based on information criteria

    Sakamoto, W

    COMPSTAT'2010 (Paris, France)  2010.8 

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  • Rによる統計的シミュレーション入門

    医学統計研究会特定主題シンポジウム「臨床評価における計算環境Rとその周辺:S-Plusによる妥当性確認」  2010.6 

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  • シミュレーションの前と後:統計的観点

    五十川直樹, 坂本 亘, 後藤昌司

    医学統計研究会特定主題セミナー「臨床評価過程におけるシミュレーションとその実際」  2010.3 

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  • Bayes 流接近法の基礎

    坂本 亘

    医学統計研究会特定主題シンポジウム「臨床評価におけるBayes 流接近法」  2009.11 

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  • 生存時間解析に必要な統計的基礎知識

    坂本 亘

    医学統計研究会特定主題シンポジウム「患者像に基づく臨床評価の過程:癌患者の治療を中心に」  2009.9 

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  • Bayes 流接近法の基礎

    坂本 亘

    医学統計研究会特定主題シンポジウム「臨床評価におけるBayes 流接近法」  2009.3 

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  • Selecting an appropriate transformation of responses for fitting a semiparametric mixed model Invited

    Sakamoto, W

    International Joint Session of CSA, JSS and KSS. (Taipei)  2008.12 

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  • 「メタボリック・シンドローム」診断基準の統計的問題

    坂本 亘

    第92回行動計量学会シンポジウム  2008.6 

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  • 「メタボリック・シンドローム」の統計的論拠について

    坂本 亘

    大分統計談話会・第37回大会  2008.2 

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  • Rによる統計的シミュレーション入門

    坂本 亘

    BRA特定主題セミナー「臨床評価における計算環境Rとその周辺:S-Plusによる妥当性確認」  2007.12 

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  • 「医学統計学」とその周辺:誤用と対策

    坂本 亘

    医学統計研究会定例シンポジウム「医療で必要とされる統計的基礎知識2007」  2007.10 

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  • 生存時間解析に必要な統計的基礎知識

    坂本 亘

    医学統計研究会特定主題セミナー「癌治療の評価における生存時間解析の方法  2007.9 

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  • Rによる統計的シミュレーション入門

    坂本 亘

    医学統計研究会特定主題シンポジウム「臨床評価における計算環境Rとその周辺:S-Plusによる妥当性確認」  2007.3 

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  • 「医学統計学」とその周辺:誤用と対策

    坂本 亘

    医学統計研究会定例シンポジウム「医療で必要とされる統計的基礎知識2006」  2006.11 

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  • 経験Bayes法による多変量適応的回帰スプラインの推定

    坂本 亘

    大分統計談話会・第34回大会  2006.10 

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  • Selecting basis and knots in MARS with an empirical Bayes method

    Sakamoto, W

    Conference on Nonparametric Statistics and Related Topics (Carleton University, Ottawa, Canada)  2006.9 

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  • Bayes 流樹木構造接近法の有用性

    大分統計談話会・第32回大会  2005.10 

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  • Diagnosing non-linear regression structure with power additive smoothing splines

    Sakamoto, W

    14th International Workshop on Matrices and Statistics (Auckland, NZ)  2005.3 

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  • 非正規型ノンパラメトリック回帰問題における平滑化パラメータの選定 : 近似最大周辺尤度法

    坂本 亘

    大分統計談話会・第26回大会  2002.10 

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  • ノンパラメトリック回帰の諸法

    坂本 亘

    大分統計談話会・第20回会合  1999.9.30 

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Awards

  • IASC Young Researchers Award

    2006.9   COMPSTAT2006 (Rome, Italy)  

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Research Projects

  • Effective inference and selection of statistical models to represent latent structure in spatial data

    Grant number:26330042  2014.04 - 2017.03

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (C)  Grant-in-Aid for Scientific Research (C)

    Sakamoto Wataru

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    Grant amount:\4160000 ( Direct expense: \3200000 、 Indirect expense:\960000 )

    Some methods of making effective inference and selection in statistical models with high-dimensional latent variables are considered to reveal complicated latent structure in spacial and geographical data. It was shown that the method of detecting regions using estimated spatial effect, proposed for application to disease mapping data, had higher possibility of detecting regions with high risk than existing methods. Also it was suggested that the model selection method considered in the analysis of cancer mortality data with age-period-cohort (APC) models could estimate each effect appropriately, and give a new knowledge for interpretation.

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  • Selecting an optimal mixed effect model based on simulation : theory and applications

    Grant number:21500275  2009 - 2011

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (C)  Grant-in-Aid for Scientific Research (C)

    SAKAMOTO Wataru

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    Grant amount:\3250000 ( Direct expense: \2500000 、 Indirect expense:\750000 )

    Mixed effect models are useful for analysis of data which are measured repeatedly for each individual, and have been applied to various fields such as medical and biological sciences. However, how to select an optimal mixed effect model has not been fully examined. We proposed a criterion to select such an optimal model, which would be appropriate in theory and practically more useful. The criterion can be computed approximately by using random numbers. We confirmed that the proposed criterion should lessen risk of choosing a parsimonious model wrongly.

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  • がん対策推進基本計画の効果検証と目標設定に関する研究

    2014 - 2016

    厚生労働省  厚生労働科学研究費  H26-がん政策-一般-015

    加茂憲一(研究代表者), 田中純子, 高橋秀人, 坂本亘, 片野田耕太, 伊藤ゆり, 雑賀公美子, 松田 彩子, 伊森晋平

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  • ノンパラメトリック回帰による多次元構造の探索:推定方式の改良と実装

    Grant number:17700280  2005 - 2007

    日本学術振興会  科学研究費助成事業 若手研究(B)  若手研究(B)

    坂本 亘

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    Grant amount:\3100000 ( Direct expense: \3100000 )

    本研究は、非線形構造の探索・診断という観点からノンパラメトリック回帰の方法論を再整備し、ノンパラメトリック回帰をより強力な探索的データ解析の道具にすることを目的とした。とくに交互作用効果を含む多次元ノンパラメトリック回帰モデルの有用性を探った。
    前年度までに提案・開発した、多変量適応的回帰スプライン(MARS)における経験Bayes法による基底関数および節点の選定方式は、Friedman による従来の方式(一般化交差確認法の利用)での回帰構造の解釈などの難点を克服することを目標としている。これについて、本年度は以下のような研究を行った。
    1.変数寄与の測度の再検討
    回帰構造の解釈に必要となる、各々の説明変数の寄与(主効果・交互作用効果)の測度について、従来の「相対重要度」はかなりアドホックな定義であった。より合理的・包括的な測度として、分散分析などで用いられる平方和分解に類似した変動の分解に基づいて、条件付き分散を用いて定義することを検討した。
    2.シミュレーションなどによる性能評価
    前述の条件付き分散に基づく測度を用いて、従来の方式(Friedmanが提案した交差確認法などによる方法)に比べて、回帰構造、とくに交互作用効果を正しく抽出できることを実証した。

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  • ノンパラメトリック回帰による非線形構造の探索とその実装

    Grant number:14780171  2002 - 2004

    日本学術振興会  科学研究費助成事業 若手研究(B)  若手研究(B)

    坂本 亘

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    Grant amount:\3100000 ( Direct expense: \3100000 )

    本研究の目標は,データの背後にある複雑な非線形構造を探索・抽出するための道具として,ノンパラメトリック回帰,とくに平滑化スプラインの有用性を実証し,その方法を実装したアプリケーションを開発することであった。
    本年度に実施した研究内容は以下のとおりである。
    1.応答のベキ変換による加法モデルの拡張
    加法モデルにおける仮定(加法性,分散均一性,正規性など)の妥当性を評価するために,ベキ加法化平滑化スプラインやベキ重み付き平滑化スプラインなどを提案し,Fortranプログラムによる実装を行った。応答のベキ変換パラメータを分散・平滑化パラメータと同時に最大周辺尤度法(経験Bayes法)によって選定する。ベキ加法化平滑化スプラインでは周辺尤度の正確な計算は困難であるため,その近似方式を提案した。本方式の近似精度,および本方式を用いた場合の加法関数の推定性能を,事例研究やシミュレーションを通じて評価し,推定されるベキ変換が非線形構造を考慮しながら加法性や分散均一性などの要件を達成するという妥当な結果を得ることができた。
    2.ABIC(赤池Bayes情報量基準)による最適なモデルの選定
    平滑化スプラインの線形混合モデルによる表現を利用することにより,包括的な階層型のモデル族を構築することが可能となる。モデルが非線形(ノンパラメトリック)成分を含むかどうかの診断は,線形混合モデルのランダム効果に関する診断に帰着され,必要となる分散パラメータの個数の選定にはABICを用いることができる。本方式の有用性を事例研究などにより確認した。

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  • 非線形構造の探索・抽出のためのノンパラメトリック回帰とその有用性の研究

    Grant number:12780177  2000 - 2001

    日本学術振興会  科学研究費助成事業 奨励研究(A)  奨励研究(A)

    坂本 亘

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    Grant amount:\1700000 ( Direct expense: \1700000 )

    ノンパラメトリック回帰の代表的な方法であるスプライン平滑化では,平滑化パラメータが推定関数の滑らかさを制御する.本研究では,制限付き最尤推定法(REML)による平滑化パラメータの選定方式に着目し,非線形構造の探索・抽出という観点からその性能評価を行った.そして,REMLによる選定方式を用いることで,スプライン平滑化が探索的データ解析の有用な道具となる可能性を追求してきた.
    13年度の研究で得られた知見・成果は以下のとおりである.
    1.シミュレーションによるREMLの性能評価
    REMLによる非線形構造や相関構造の抽出能力,さらに関数および分散・相関パラメータの推定性能を評価した.その結果,REMLは非線形構造をほぼ適切に抽出し,相関がある場合にもこれを適切に検出することができること,さらにREMLは標本サイズが比較的小さい場合にも有用であることがわかった.平滑化スプラインとREMLの組み合わせにより,ノンパラメトリック回帰は非線形構造の探索・診断を行うための道具としての有用性をもつといえる.
    今後は以上の成果について学会発表や投稿論文による報告を行う予定である.加法モデルや交互作用モデルなど種々のモデルヘのREMLの適応とその評価についても今後の検討課題としたい.
    2.非正規型ノンパラメトリック回帰モデルヘのREMLの適用についての考察
    非正規型平滑化スプライン・モデルを一般化線形混合効果モデルによって表現し,回帰関数を求めるための反復方程式に基づいて構成される近似的な制限付き対数尤度を最大化する.簡単な数値実験を行ったところ,観測が同じ説明変数値で反復される場合にはREMLによって平滑化パラメータが良好に選定されるのに対して,異なる説明変数値において観測される場合にはやや滑らかな関数を推定することがわかった.
    今後は上記の問題点の改良を行い,より包括的な性能評価を行う予定である.

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Academic Activities

  • Computational Statistics: Editor-in-Chief

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    2018.7.1 - 2020.12.31

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  • 2015年度統計関連学会連合大会(運営委員長)

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    2015.9.6 - 2015.9.9

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    2018.4 - 2021.3

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    2018.1.1 - 2018.12.31

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    2015.4.1 - 2018.6.30

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    2012.4 - 2015.3

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    2009.1 - 2014.12

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    2001.8

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