|Shuichi Kawano||Last modified date：2023.03.14|
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Reseacher Profiling Tool Kyushu University Pure
Ph.D (Functional Mathematics)
Country of degree conferring institution (Overseas)
Field of Specialization
Statistical Sciences, Data Science, Computational Statistics
ORCID(Open Researcher and Contributor ID)
My research is to develop the methods of data analysis in terms of statistics in order to obtain useful information and knowledge from data. Specifically, I develop multivariate analyses with sparse estimation for high-dimensional and small sample situations where the number of parameters in the statistical model is larger than the sample size. I also study Bayesian modeling, which can treat prior information of statistical models, and computational algorithms for numerically obtaining parameter estimates.
Research InterestsMembership in Academic Society
- Developing statistical modeling via sparse estimation
keyword : high-dimensional data, variable selection, Bayesian modeling, information criteria
- Developing bioinformatics methods based on statistical modeling
keyword : gene analysis, microbiota analysis, gene network
|1.||Murayama, K., Kawano, S., Sparse Bayesian learning with weakly informative hyperprior and extended predictive information criterion, IEEE Transactions on Neural Networks and Learning Systems, 10.1109/TNNLS.2021.3131357, early access, 2023.12, [URL].|
|2.||Yoshikawa, K., Kawano, S., Sparse reduced-rank regression for simultaneous rank and variable selection via manifold optimization, Computational Statistics, 10.1007/s00180-022-01216-5, 38, 1, 53-75, 2023.03, [URL].|
|3.||Kim, D., Kawano, S., Ninomiya, Y., Smoothly varying regularization, Computational Statistics & Data Analysis, 10.1016/j.csda.2022.107644, 179, 107644, 2023.03, [URL].|
|4.||Okazaki, A., Kawano, S., Multi-task learning for compositional data via sparse network lasso, Entropy, 10.3390/e24121839, 24, 12, 1839, 2022.12, [URL].|
|5.||Shimamura, K., Kawano, S., Bayesian sparse convex clustering via global-local shrinkage priors, Computational Statistics, 10.1007/s00180-021-01101-7, 36, 4, 2671-2699, 2021.12, [URL].|
|6.||Yoshikawa, K., Kawano, S., Multilinear common component analysis via Kronecker product representation, Neural Computation, 10.1162/neco_a_01425, 33, 10, 2853-2880, 2021.10, [URL].|
|7.||Kawano, S., Sparse principal component regression via singular value decomposition approach, Advances in Data Analysis and Classification, 10.1007/s11634-020-00435-2, 15, 3, 795-823, 2021.09, [URL].|
|8.||Yoshida, H., Kawano, S., Ninomiya, Y., Discriminant analysis via smoothly varying regularization, Proceedings in the 13th KES International Conference on Intelligent Decision Technologies, 10.1007/978-981-16-2765-1_37, 238, 441-455, 2021.07, [URL].|
|9.||Wu, S., Shimamura, K., Yoshikawa, K., Murayama, K., Kawano, S., Variable fusion for Bayesian linear regression via spike-and-slab priors, Proceedings in the 13th KES International Conference on Intelligent Decision Technologies, 10.1007/978-981-16-2765-1_41, 238, 491-501, 2021.07, [URL].|
|10.||Kojima, S., Yoshikawa, K., Ito, J., Nakagawa, S., Parrish, N.F., Horie, M., Kawano, S., Tomonaga, K., Virus-like insertions with sequence signatures similar to those of endogenous non-retroviral RNA viruses in the human genome, Proceedings of the National Academy of Sciences of the United States of America, 10.1073/pnas.2010758118, 118, 5, e2010758118, 2021.02, [URL].|
|11.||Kato, A., Adachi, S., Kawano, S., Takeshima, K., Watanabe, M., Kitazume, S., Sato, R., Kusano, H., Koyanagi, N., Maruzuru, Y., Arii, J., Hatta, T., Natsume, T., Kawaguchi, Y., Identification of a herpes simplex virus 1 gene encoding neurovirulence factor by chemical proteomics, Nature Communications, 10.1038/s41467-020-18718-9, 11, 4894, 2020.09, [URL].|
|13.||Shimamura, K., Ueki, M., Kawano, S., Konishi, S., Bayesian generalized fused lasso modeling via NEG distribution, Communications in Statistics - Theory and Methods, 10.1080/03610926.2018.1489056, 48, 16, 4132-4153, 2019.08, [URL].|
|14.||Kawano, S., Fujisawa, H., Takada, T., Shiroishi, T., Sparse principal component regression for generalized linear models, Computational Statistics & Data Analysis, 10.1016/j.csda.2018.03.008, 124, 180-195, 2018.08, [URL].|
|15.||Ninomiya, Y., Kawano, S., AIC for the Lasso in generalized linear models, Electronic Journal of Statistics, 10.1214/16-EJS1179, 10, 2, 2537-2560, 2016.09, [URL].|
|16.||Kawano, S., Fujisawa, H., Takada, T., Shiroishi, T., Sparse principal component regression with adaptive loading, Computational Statistics & Data Analysis, 10.1016/j.csda.2015.03.016 , 89, 192-203, 2015.09, [URL].|
|17.||Kawano, S., Selection of tuning parameters in bridge regression models via Bayesian information criterion, Statistical Papers, 10.1007/s00362-013-0561-7, 55, 4, 1207-1223, 2014.11, [URL].|
|18.||Kawano, S., Semi-supervised logistic discrimination via labeled data and unlabeled data from different sampling distributions, Statistical Analysis and Data Mining, 10.1002/sam.11204, 6, 6, 472-481, 2013.12, [URL].|
|19.||Kawano, S., Misumi, T., Konishi, S., Semi-supervised logistic discrimination via graph-based regularization, Neural Processing Letters, 10.1007/s11063-012-9231-3, 36, 3, 203-216, 2012.12, [URL].|
|20.||Kawano, S., Shimamura, T., Niida, A., Imoto, S., Yamaguchi, R., Nagasaki, M., Yoshida, R., Print, C., Miyano, S., Identifying gene pathways associated with cancer characteristics via sparse statistical methods, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 10.1109/TCBB.2012.48, 9, 4, 966-972, 2012.07, [URL].|
Works, Software and Database
|1.||R package CSNL. This R package computes multi-task learning models for compositional data via sparse network lasso.
|2.||R package SVaRu. This R package computes nonparametric regression models with automatically determined smoothing parameters. The hyper-tuning parameters are also optimized by generalized information criteria.
|3.||R package spcr-svd. This R package computes the sparse principal component regression via singular value decomposition approach. The regularization parameters are also optimized by cross-validation.
|4.||R package MCCA. This R package computes multilinear common component analysis via Kronecker product representation.
|5.||R package neggfl. This R package computes the Bayesian generalized fused lasso regression based on a normal-exponential-gamma (NEG) prior distribution.
|6.||R package RVSManOpt. This R package computes the sparse reduced-rank factor regression based on manifold optimization. This package can perform estimating the rank of the coefficient matrix, selecting the number of explanatory variables which composes factors included in the regression, and selecting the number of the factors are relevant with the response variables.
|7.||R package sAIC. This R package computes the Akaike information criterion for the generalized linear models (logistic regression, Poisson regression, and Gaussian graphical models) estimated by the lasso.
|8.||R package spcr. This R package computes the sparse principal component regression. The regularization parameters are also optimized by cross-validation.
- American Statistical Association
- Japanese Society of Applied Statistics
- The Japan Statistical Society
- The Behaviormetric Society
- Japanese Society of Computational Statistics
- Research Association of Statistical Sciences
- The Institute of Electronics, Information and Communication Engineers
I am in charge of the Department of Mathematics in the Faculty of Science for undergraduate students and the Graduate School of Mathematics for graduate students. I conduct educational activities for students through lecture classes and seminars.
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