2021/04/20 更新

写真a

フシキ タダヨシ
伏木 忠義
FUSHIKI Tadayoshi
所属
教育研究院 人文社会科学系 教育学系列 准教授
教育学研究科 教科教育専攻 准教授
教育学部 自然情報講座 准教授
職名
准教授
外部リンク

学位

  • 博士(工学) ( 2003年3月   東京大学 )

研究分野

  • 自然科学一般 / 応用数学、統計数学

経歴(researchmap)

  • 情報・システム研究機構 統計数理研究所

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  • 新潟大学 教育学部 自然情報講座

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経歴

  • 新潟大学   教育学部 自然情報講座   准教授

    2014年4月 - 現在

  • 新潟大学   教育学研究科 教科教育専攻   准教授

    2014年4月 - 現在

 

論文

  • On the Selection of the Regularization Parameter in Stacking

    Tadayoshi Fushiki

    NEURAL PROCESSING LETTERS53 ( 1 ) 37 - 48   2021年2月

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    記述言語:英語   掲載種別:研究論文(学術雑誌)   出版者・発行元:SPRINGER  

    Stacking is a model combination technique to improve prediction accuracy. Regularization is usually necessary in stacking because some predictions used in the model combination provide similar predictions. Cross-validation is generally used to select the regularization parameter, but it incurs a high computational cost. This paper proposes two simple low computational cost methods for selecting the regularization parameter. The effectiveness of the methods is examined in numerical experiments. Asymptotic results in a particular setting are also shown.

    DOI: 10.1007/s11063-020-10378-6

    Web of Science

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  • A note on the properties of estimators in missing data analysis

    Tadayoshi Fushiki

    COMMUNICATIONS IN STATISTICS-THEORY AND METHODS   2020年11月

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    記述言語:英語   掲載種別:研究論文(学術雑誌)   出版者・発行元:TAYLOR & FRANCIS INC  

    In the missing mechanism, missing at random (MAR) is sometimes assumed when data has missing values. When MAR holds and the true distribution belongs to the assumed statistical model, the maximum likelihood estimator based on the observed data has consistency. Based on a weaker condition than MAR, this study investigates the properties of the estimators obtained by applying the maximum likelihood method and the Bayesian method when the true distribution does not belong to the statistical model.

    DOI: 10.1080/03610926.2020.1854305

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  • Nonresponse Bias Adjustment in Regression Analysis

    Tadayoshi Fushiki, Tadahiko Maeda

    JOURNAL OF STATISTICAL THEORY AND PRACTICE14 ( 2 )   2020年2月

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    記述言語:英語   掲載種別:研究論文(学術雑誌)   出版者・発行元:SPRINGER  

    Nonresponse is an unavoidable problem in most sample surveys. If the proportion of nonrespondents is very small, nonresponse bias may be negligible. However, nonresponse rates in sample surveys have recently increased in many countries. Thus, methods for dealing with nonresponse bias are becoming an important topic. Regression analysis is often used to analyze survey data. In this paper, we discuss regression analysis with unit nonresponse. The least square estimator of regression coefficients may be asymptotically biased if nonresponse is not ignorable. In this paper, we establish a sufficient condition that a consistent estimator of regression coefficients is obtained. This condition can be determined from a causal diagram. Furthermore, we examine the results of this study by numerical experiments.

    DOI: 10.1007/s42519-020-0086-z

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  • NONRESPONSE ADJUSTMENTS FOR ESTIMATES OF PROPORTIONS IN THE 2010 SURVEY ON STRATIFICATION AND SOCIAL PSYCHOLOGY

    Fushiki Tadayoshi, Maeda Tadahiko

    Behaviormetrika41 ( 1 ) 99 - 114   2014年

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    記述言語:英語   出版者・発行元:日本行動計量学会  

    The main purpose of this study is to investigate influence of nonresponse in the "Interview Survey for Stratification and Social Psychology in 2010" (SSP-I2010 Survey). Now, social stratification is one of main research themes in the study of Japanese society, and the SSP-I2010 Survey provides basic data to study social stratification and people's views on economic inequality in Japan. From a target sample of 3,500, approximately half (1,737) did not respond in the survey, thus nonresponse bias is a serious concern. From a survey methodological viewpoint, studies applying methods for dealing with nonresponse to Japanese surveys are few. Therefore many empirical studies with nonresponse bias adjustment are needed to understand influence of nonresponse in Japanese surveys. In an attempt to reduce the nonresponse bias in the SSP-I2010 Survey, we used two bias adjustment methods using information on both survey locations and individuals as auxiliary variables. The effectiveness of the bias adjustment methods was evaluated by a simulation and several items of the SSP-I2010 Survey where the values of population proportions are known. In this study, stratum identification was relatively insensitive to bias adjustment. On the other hand, the estimates of the proportion of people who accept the economic inequality increased by bias adjustment.

    DOI: 10.2333/bhmk.41.99

    CiNii Article

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  • Estimation of prediction error by using K-fold cross-validation

    Tadayoshi Fushiki

    STATISTICS AND COMPUTING21 ( 2 ) 137 - 146   2011年4月

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    記述言語:英語   掲載種別:研究論文(学術雑誌)   出版者・発行元:SPRINGER  

    Estimation of prediction accuracy is important when our aim is prediction. The training error is an easy estimate of prediction error, but it has a downward bias. On the other hand, K-fold cross-validation has an upward bias. The upward bias may be negligible in leave-one-out cross-validation, but it sometimes cannot be neglected in 5-fold or 10-fold cross-validation, which are favored from a computational standpoint. Since the training error has a downward bias and K-fold cross-validation has an upward bias, there will be an appropriate estimate in a family that connects the two estimates. In this paper, we investigate two families that connect the training error and K-fold cross-validation.

    DOI: 10.1007/s11222-009-9153-8

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  • Bayesian bootstrap prediction

    Tadayoshi Fushiki

    JOURNAL OF STATISTICAL PLANNING AND INFERENCE140 ( 1 ) 65 - 74   2010年1月

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    記述言語:英語   掲載種別:研究論文(学術雑誌)   出版者・発行元:ELSEVIER SCIENCE BV  

    In this paper, bootstrap prediction is adapted to resolve some problems in small sample datasets. The bootstrap predictive distribution is obtained by applying Breiman's bagging to the plug-in distribution with the maximum likelihood estimator. The effectiveness of bootstrap prediction has previously been shown, but some problems may arise when bootstrap prediction is constructed in small sample datasets. In this paper, Bayesian bootstrap is used to resolve the problems. The effectiveness of Bayesian bootstrap prediction is confirmed by some examples. These days, analysis of small sample data is quite important in various fields. In this paper, some datasets are analyzed in such a situation. For real datasets, it is shown that plug-in prediction and bootstrap prediction provide very poor prediction when the sample size is close to the dimension of parameter while Bayesian bootstrap prediction provides stable prediction. (C) 2009 Elsevier B.V. All rights reserved.

    DOI: 10.1016/j.jspi.2009.06.007

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  • Estimation of Positive Semidefinite Correlation Matrices by Using Convex Quadratic Semidefinite Programming

    Tadayoshi Fushiki

    NEURAL COMPUTATION21 ( 7 ) 2028 - 2048   2009年7月

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    記述言語:英語   掲載種別:研究論文(学術雑誌)   出版者・発行元:MIT PRESS  

    The correlation matrix is a fundamental statistic that used in many fields. For example, GroupLens, a collaborative filtering system, uses the correlation between users for predictive purposes. Since the correlation is a natural similarity measure between users, the correlation matrix may be used as the Gram matrix in kernel methods. However, the estimated correlation matrix sometimes has a serious defect: although the correlation matrix is originally positive semidefinite, the estimated one may not be positive semidefinite when not all ratings are observed. To obtain a positive semidefinite correlation matrix, the nearest correlation matrix problem has recently been studied in the fields of numerical analysis and optimization. However, statistical properties are not explicitly used in such studies. To obtain a positive semidefinite correlation matrix, we assume an approximate model. By using the model, an estimate is obtained as the optimal point of an optimization problem formulated with information on the variances of the estimated correlation coefficients. The problem is solved by a convex quadratic semidefinite program. A penalized likelihood approach is also examined. The MovieLens data set is used to test our approach.

    DOI: 10.1162/neco.2009.04-08-765

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  • A maximum likelihood approach to density estimation with semidefinite programming

    Tadayoshi Fushiki, Shingo Horiuchi, Takashi Tsuchiya

    NEURAL COMPUTATION18 ( 11 ) 2777 - 2812   2006年11月

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    記述言語:英語   掲載種別:研究論文(学術雑誌)   出版者・発行元:MIT PRESS  

    Density estimation plays an important and fundamental role in pattern recognition, machine learning, and statistics. In this article, we develop a parametric approach to univariate (or low-dimensional) density estimation based on semidefinite programming (SDP). Our density model is expressed as the product of a nonnegative polynomial and a base density such as normal distribution, exponential distribution, and uniform distribution. When the base density is specified, the maximum likelihood estimation of the polynomial is formulated as a variant of SDP that is solved in polynomial time with the interior point methods. Since the base density typically contains just one or two parameters, computation of the maximum likelihood estimate reduces to a one- or two-dimensional easy optimization problem with this use of SDP. Thus, the rigorous maximum likelihood estimate can be computed in our approach. Furthermore, such conditions as symmetry and unimodality of the density function can be easily handled within this framework. AIC is used to choose the best model. Through applications to several instances, we demonstrate flexibility of the model and performance of the proposed procedure. Combination with a mixture approach is also presented. The proposed approach has possible other applications beyond density estimation. This point is clarified through an application to the maximum likelihood estimation of the intensity function of a nonstationary Poisson process.

    DOI: 10.1162/neco.2006.18.11.2777

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  • Bootstrap prediction and Bayesian prediction under misspecified models

    Tadayoshi Fushiki

    Bernoulli11 ( 4 ) 747 - 758   2005年8月

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    掲載種別:研究論文(学術雑誌)  

    We consider a statistical prediction problem under misspecified models. In a sense, Bayesian prediction is an optimal prediction method when an assumed model is true. Bootstrap prediction is obtained by applying Breiman's 'bagging' method to a plug-in prediction. Bootstrap prediction can be considered to be an approximation to the Bayesian prediction under the assumption that the model is true. However, in applications, there are frequently deviations from the assumed model. In this paper, both prediction methods arc compared by using the Kullback-Leibler loss under the assumption that the model does not contain the true distribution. We show that bootstrap prediction is asymptotically more effective than Bayesian prediction under misspecified models. © 2005 ISI/BS.

    DOI: 10.3150/bj/1126126768

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  • Nonparametric bootstrap prediction

    Tadayoshi Fushiki, Fumiyasu Komaki, Kazuyuki Aihara

    Bernoulli11 ( 2 ) 293 - 307   2005年4月

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    掲載種別:研究論文(学術雑誌)  

    Ensemble learning has recently been intensively studied in the field of machine learning. 'Bagging' is a method of ensemble learning and uses bootstrap data to construct various predictors. The required prediction is then obtained by averaging the predictors. Harris proposed using this technique with the parametric bootstrap predictive distribution to construct predictive distributions, and showed that the parametric bootstrap predictive distribution gives asymptotically better prediction than a plug-in distribution with the maximum likelihood estimator. In this paper, we investigate nonparametric bootstrap predictive distributions. The nonparametric bootstrap predictive distribution is precisely that obtained by applying bagging to the statistical prediction problem. We show that the nonparametric bootstrap predictive distribution gives predictions asymptotically as good as the parametric bootstrap predictive distribution. © 2005 ISI/BS.

    DOI: 10.3150/bj/1116340296

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  • On parametric bootstrapping and Bayesian prediction

    Tadayoshi Fushiki, Fumiyasu Komaki, Kazuyuki Aihara

    Scandinavian Journal of Statistics31 ( 3 ) 403 - 416   2004年9月

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    掲載種別:研究論文(学術雑誌)  

    We investigate bootstrapping and Bayesian methods for prediction. The observations and the variable being predicted are distributed according to different distributions. Many important problems can be formulated in this setting. This type of prediction problem appears when we deal with a Poisson process. Regression problems can also be formulated in this setting. First, we show that bootstrap predictive distributions are equivalent to Bayesian predictive distributions in the second-order expansion when some conditions are satisfied. Next, the performance of predictive distributions is compared with that of a plug-in distribution with an estimator. The accuracy of prediction is evaluated by using the Kullback-Leibler divergence. Finally, we give some examples.

    DOI: 10.1111/j.1467-9469.2004.02_127.x

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  • A phenomenon like stochastic resonance in the process of spike-timing dependent synaptic plasticity

    Tadayoshi Fushiki, Kazuyuki Aihara

    IEICE Transactions on Fundamentals of Electronics, Communications and Computer SciencesE85-A ( 10 ) 2377 - 2380   2002年10月

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    掲載種別:研究論文(学術雑誌)  

    The stability of propagating precisely timed spikes from the viewpoint of spike timing dependent synaptic plasticity (STDP) was investigated. A phenomenon similar to stochastic resonance with respect to optimal level of background noise for learning was present on STDP. It was found that the noise can be related to learning by STDP and also supports the possibility of temporal spike coding in the brain.

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