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ORCIDHirokazu Yanagihara
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2020 – today
- 2023
[c11]Hirokazu Yanagihara, Isamu Nagai, Keisuke Fukui, Yuta Hijikawa:
Ridge parameter optimization using a modified Cp statistic in multivariate generalized ridge regression for the GMANOVA model. KES 2023: 1651-1660- [c10]Mariko Yamamura, Mineaki Ohishi
, Hirokazu Yanagihara:
Additive Poisson regression via forced categorical covariates and generalized fused Lasso. KES 2023: 1987-1996 - 2022
- [j9]Kyohei Mochizuki, Hirokazu Yanagihara:
Confidence intervals in multiple linear regression conditioned on the selected model via the kick-one-out method. Int. J. Knowl. Eng. Soft Data Paradigms 7(2): 95-114 (2022) - 2021
- [c9]Ryoya Oda, Hirokazu Yanagihara:
A Consistent Likelihood-Based Variable Selection Method in Normal Multivariate Linear Regression. KES-IDT 2021: 391-401 - [c8]Hirokazu Yanagihara, Ryoya Oda:
Coordinate Descent Algorithm for Normal-Likelihood-Based Group Lasso in Multivariate Linear Regression. KES-IDT 2021: 429-439 - [c7]Mineaki Ohishi, Kensuke Okamura, Yoshimichi Itoh, Hirokazu Yanagihara:
Optimizations for Categorizations of Explanatory Variables in Linear Regression via Generalized Fused Lasso. KES-IDT 2021: 457-467 - [c6]Mariko Yamamura, Mineaki Ohishi, Hirokazu Yanagihara:
Spatio-Temporal Adaptive Fused Lasso for Proportion Data. KES-IDT 2021: 479-489 - 2020
- [j8]Ryoya Oda, Yuya Suzuki, Hirokazu Yanagihara, Yasunori Fujikoshi:
A consistent variable selection method in high-dimensional canonical discriminant analysis. J. Multivar. Anal. 175 (2020) - [c5]Mineaki Ohishi, Hirokazu Yanagihara, Hirofumi Wakaki:
Optimization of Generalized Cp Criterion for Selecting Ridge Parameters in Generalized Ridge Regression. KES-IDT 2020: 267-278 - [c4]Keisuke Fukui, Mineaki Ohishi, Mariko Yamamura, Hirokazu Yanagihara:
A Fast Optimization Method for Additive Model via Partial Generalized Ridge Regression. KES-IDT 2020: 279-290
2010 – 2019
- 2017
- [j7]Isamu Nagai, Katsuyuki Takahashi, Hirokazu Yanagihara:
Information criterion-based non-hierarchical clustering. Int. J. Knowl. Eng. Soft Data Paradigms 6(1): 1-43 (2017) - [j6]Hirokazu Yanagihara, Ryoya Oda, Yusuke Hashiyama, Yasunori Fujikoshi:
High-dimensional asymptotic behavior of the difference between the log-determinants of two Wishart matrices. J. Multivar. Anal. 157: 70-86 (2017) - 2016
- [c3]Hirokazu Yanagihara:
A High-dimensionality-adjusted Consistent Cp-type Statistic for Selecting Variables in a Normality-assumed Linear Regression with Multiple Responses. KES 2016: 1096-1105 - [c2]Mariko Yamamura, Hirokazu Yanagihara, Hiroko Kato Solvang, Nils Øien, Tore Haug:
Canonical Correlation Analysis for Geographical and Chronological Responses. KES 2016: 1351-1360 - 2014
- [j5]Yasunori Fujikoshi, Tetsuro Sakurai, Hirokazu Yanagihara:
Consistency of high-dimensional AIC-type and Cp-type criteria in multivariate linear regression. J. Multivar. Anal. 123: 184-200 (2014) - [j4]Muni S. Srivastava, Hirokazu Yanagihara, Tatsuya Kubokawa:
Tests for covariance matrices in high dimension with less sample size. J. Multivar. Anal. 130: 289-309 (2014) - 2012
- [j3]Hirokazu Yanagihara:
A non-iterative optimization method for smoothness in penalized spline regression. Stat. Comput. 22(2): 527-544 (2012) - 2010
- [j2]Hirokazu Yanagihara, Kenichi Satoh:
An unbiased Cp criterion for multivariate ridge regression. J. Multivar. Anal. 101(5): 1226-1238 (2010) - [j1]Muni S. Srivastava, Hirokazu Yanagihara:
Testing the equality of several covariance matrices with fewer observations than the dimension. J. Multivar. Anal. 101(6): 1319-1329 (2010) - [c1]Mariko Yamamura, Hirokazu Yanagihara, Muni S. Srivastava:
Variable Selection by Cp Statistic in Multiple Responses Regression with Fewer Sample Size Than the Dimension. KES (3) 2010: 7-14
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