Language：English   Publishing type：Research paper (scientific journal)   Publisher：Psychometrika  

We propose a prenet (product-based elastic net), a novel penalization method for factor analysis models. The penalty is based on the product of a pair of elements in each row of the loading matrix. The prenet not only shrinks some of the factor loadings toward exactly zero but also enhances the simplicity of the loading matrix, which plays an important role in the interpretation of the common factors. In particular, with a large amount of prenet penalization, the estimated loading matrix possesses a perfect simple structure, which is known as a desirable structure in terms of the simplicity of the loading matrix. Furthermore, the perfect simple structure estimation via the proposed penalization turns out to be a generalization of the k-means clustering of variables. On the other hand, a mild amount of the penalization approximates a loading matrix estimated by the quartimin rotation, one of the most commonly used oblique rotation techniques. Simulation studies compare the performance of our proposed penalization with that of existing methods under a variety of settings. The usefulness of the perfect simple structure estimation via our proposed procedure is presented through various real data applications.

DOI： 10.1007/s11336-022-09868-4

Web of Science

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：WILEY  

In the field of materials science and engineering, statistical analysis and machine learning techniques have recently been used to predict multiple material properties from an experimental design. These material properties correspond to response variables in the multivariate regression model. In this study, we conduct a penalized maximum likelihood procedure to estimate model parameters, including the regression coefficients and covariance matrix of response variables. In particular, we employ l1-regularization to achieve a sparse estimation of The regression coefficients and inverse covariance matrix of response variables. In some cases, there may be a relatively large number of missing values in the response variables, owing to the difficulty of collecting data on material properties. We therefore propose a method that incorporates a correlation structure among the response variables into a statistical model to improve the prediction accuracy under the situation with missing values. The expectation maximization algorithm is also constructed, which enables application to a dataset with missing values in the responses. We apply our proposed procedure to real data consisting of 22 material properties.

DOI： 10.1002/nme.6867

Web of Science

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Language：English   Publishing type：Research paper (scientific journal)  

We consider the problem of short- and medium-term electricity demand forecasting by using past demand and daily weather forecast information. Conventionally, many researchers have directly applied regression analysis. However, interpreting the effect of weather on the demand is difficult with the existing methods. In this study, we build a statistical model that resolves this interpretation issue. A varying coefficient model with basis expansion is used to capture the nonlinear structure of the weather effect. This approach results in an interpretable model when the regression coefficients are nonnegative. To estimate the nonnegative regression coefficients, we employ nonnegative least squares. Three real data analyses show the practicality of our proposed statistical modeling. Two of them demonstrate good forecast accuracy and interpretability of our proposed method. In the third example, we investigate the effect of COVID-19 on electricity demand. The interpretation would help make strategies for energy-saving interventions and demand response.

DOI： 10.3389/fenrg.2021.724780

Language：English   Publishing type：Research paper (scientific journal)  

Language：English   Publishing type：Research paper (scientific journal)  

遺伝子ネットワークと予測精度をシミュレーションし，そのソフトウェアを公開した．

Language：Japanese   Publishing type：Research paper (scientific journal)  

Language：English   Publishing type：Research paper (scientific journal)  

Relative error estimation has been recently used in regression analysis. A crucial issue of the existing relative error estimation procedures is that they are sensitive to outliers. To address this issue, we employ the γ-likelihood function, which is constructed through γ-cross entropy with keeping the original statistical model in use. The estimating equation has a redescending property, a desirable property in robust statistics, for a broad class of noise distributions. To find a minimizer of the negative γ-likelihood function, a majorize-minimization (MM) algorithm is constructed. The proposed algorithm is guaranteed to decrease the negative γ-likelihood function at each iteration. We also derive asymptotic normality of the corresponding estimator together with a simple consistent estimator of the asymptotic covariance matrix, so that we can readily construct approximate confidence sets. Monte Carlo simulation is conducted to investigate the effectiveness of the proposed procedure. Real data analysis illustrates the usefulness of our proposed procedure.

DOI： 10.3390/e20090632

Language：English   Publishing type：Research paper (scientific journal)  

In factor regression model, the maximum likelihood estimation suffers from three disadvantages: (i) the maximum likelihood estimates are unavailable when the number of variables exceeds the number of observations, (ii) the rotation technique based on maximum likelihood estimates produces an insufficiently sparse loading matrix, and (iii) multicollinearity can occur when the estimates of unique variances (specific variances) are small because the regression coefficients are sensitive to the inverse of unique variances. To handle these problems, we propose a penalized maximum likelihood procedure. Specifically, we impose a lasso-type penalty on the factor loadings to improve the sparseness of the solution. We also introduce a penalty on unique variances, which (given the factor scores) corresponds to the ridge penalty on the regression coefficient. Theoretical properties from a prediction viewpoint of our procedure are discussed. The effectiveness of the procedure is investigated through Monte Carlo simulations. The utility of our procedure is demonstrated on real data collected by an online questionnaire.

DOI： 10.1007/s00362-016-0781-8

Language：English   Publishing type：Research paper (scientific journal)  

Echo state network (ESN) is viewed as a temporal expansion which naturally give rise to regressors of various relevance to a teacher output. We illustrate that often only a certain amount of the generated echo-regressors effectively explain the teacher output and we propose to determine the importance of the echo-regressors by a joint calculation of the individual variance contributions and Bayesian relevance using the locally regularized orthogonal forward regression (LROFR). This information can be advantageously used in a variety of ways for an analysis of an ESN structure. We present a locally regularized linear readout built using LROFR. The readout may have a smaller dimensionality than the ESN model itself, and improves robustness and accuracy of an ESN. Its main advantage is ability to determine what type of an additional readout is suitable for a task at hand. Comparison with PCA is provided too. We also propose a radial basis function (RBF) readout built using LROFR, since flexibility of the linear readout has limitations and might be insufficient for complex tasks. Its excellent generalization abilities make it a viable alternative to feed-forward neural networks or relevance-vector-machines. For cases where more temporal capacity is required we propose well studied delay&sum readout.

DOI： 10.1080/02664763.2017.1305331

Language：English   Publishing type：Research paper (scientific journal)  

Gaussian graphical modeling is popular as a means of exploring network structures, such as gene regulatory networks and social networks. An L₁ penalized maximum likelihood approach is often used to learn high-dimensional graphical models. However, the penalized maximum likelihood procedure is sensitive to outliers. To overcome this problem, we introduce a robust estimation procedure based on the γ-divergence. The proposed method has a redescending property, which is a desirable feature in robust statistics. The parameter estimation procedure is constructed using the Majorize-Minimization algorithm, which guarantees that the objective function monotonically decreases at each iteration. Extensive simulation studies show that our procedure performs much better than the existing methods, in particular, when the contamination ratio is large. Two real data analyses are used for illustration purposes.

DOI： 10.1016/j.jmva.2017.07.012

Language：English   Publishing type：Research paper (scientific journal)  

For a business to be successful, it is important for people in the business to consider how other people feel, that is, to consider interpersonal sentiment. Our research goal is to quantitatively predict the strength of interpersonal sentiment by analyzing a small amount of data on office employees, for example, their gender or age group, and data on events such as giving positive feedback on work done and sexual or power harassment without directly asking someone about their change in sentiment. In this article, we propose an interpersonal-sentiment-changing model for this quantification and propose two new analysis methods for developing prediction formulas. These methods can be used even if 90% of data is missing and in environments in which it is difficult to gather data in a comparatively short time. We also implement two visualization systems to predict how interpersonal sentiment changes for each event based on actual office data.

Language：English   Publishing type：Research paper (scientific journal)  

Language：English   Publishing type：Research paper (scientific journal)  

Event date： 2022.5

Language：Others  

Country：Other  

Event date： 2022.5

Presentation type：Public lecture, seminar, tutorial, course, or other speech  

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Event date： 2021.11

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Event date： 2021.3

Language：Japanese   Presentation type：Oral presentation (general)  

Country：Japan  

Event date： 2021.1

Language：English   Presentation type：Oral presentation (general)  

Country：Japan  

Event date： 2020.10

Language：Japanese   Presentation type：Oral presentation (general)  

Event date： 2020.10

Language：Japanese   Presentation type：Oral presentation (general)  

Event date： 2020.10

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Language：Japanese   Presentation type：Oral presentation (general)  

Event date： 2020.9

Language：Japanese   Presentation type：Oral presentation (general)  

Event date： 2020.9

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Event date： 2019.9

Language：Japanese   Presentation type：Oral presentation (general)  

Venue：滋賀大学   Country：Japan  

Event date： 2019.6

Language：English   Presentation type：Oral presentation (general)  

Country：Japan  

Event date： 2018.10

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Country：Japan  

Event date： 2018.9

Language：Japanese   Presentation type：Oral presentation (general)  

Country：Japan  

Event date： 2018.9

Language：Japanese   Presentation type：Oral presentation (general)  

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Event date： 2018.6

Language：English   Presentation type：Oral presentation (general)  

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Event date： 2018.5

Language：Japanese   Presentation type：Oral presentation (general)  

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Event date： 2017.12

Language：English   Presentation type：Oral presentation (general)  

Country：Japan  

Event date： 2017.9

Language：Japanese  

Country：Japan  

Event date： 2017.9

Language：Japanese   Presentation type：Oral presentation (general)  

Country：Japan  

Event date： 2017.9

Language：Japanese   Presentation type：Oral presentation (general)  

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Event date： 2017.8

Language：English   Presentation type：Oral presentation (general)  

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Language：English   Presentation type：Oral presentation (general)  

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Event date： 2017.3

Language：Japanese   Presentation type：Oral presentation (invited, special)  

Country：Japan  

Event date： 2017.3

Language：Japanese   Presentation type：Oral presentation (general)  

Country：Japan  

Event date： 2017.3

Language：Japanese   Presentation type：Oral presentation (general)  

Country：Japan  

Event date： 2016.12

Language：Japanese   Presentation type：Oral presentation (invited, special)  

Country：Japan  

Event date： 2016.9

Language：Japanese   Presentation type：Oral presentation (general)  

Venue：金沢大学   Country：Japan  

Kanazawa University

Event date： 2016.8

Language：English   Presentation type：Oral presentation (general)  

Country：Japan  

Event date： 2016.6

Language：English   Presentation type：Oral presentation (general)  

Country：Japan  

Event date： 2016.5

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Language：Others  

Country：Other  

Language：Japanese   Presentation type：Oral presentation (invited, special)  

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Language：Japanese   Publishing type：Internal/External technical report, pre-print, etc.  

JEPXのスポット市場での電力調達に使える需要予測ソフト．

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ロバストかつスパースなガウシアングラフィカルモデル

ロバストかつスパースなガウシアングラフィカルモデル

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正則化法による因子分析

正則化法による因子分析

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Type：Peer review 

Number of peer-reviewed articles in foreign language journals：15

Type：Peer review 

Number of peer-reviewed articles in foreign language journals：5

Number of peer-reviewed articles in Japanese journals：0

Proceedings of International Conference Number of peer-reviewed papers：0

Proceedings of domestic conference Number of peer-reviewed papers：0