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Liebscher E, Okhrin O. Semiparametric estimation of the high-dimensional elliptical distribution. J MULTIVARIATE ANAL 2022. [DOI: 10.1016/j.jmva.2022.105142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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Egashira K. Asymptotic properties of multiclass support vector machine under high dimensional settings. COMMUN STAT-SIMUL C 2022. [DOI: 10.1080/03610918.2022.2066693] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Affiliation(s)
- Kento Egashira
- Degree Programs in Pure and Applied Sciences, Graduate School of Science and Technology, University of Tsukuba, Ibaraki, Japan
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Nakayama Y, Yata K, Aoshima M. Clustering by principal component analysis with Gaussian kernel in high-dimension, low-sample-size settings. J MULTIVARIATE ANAL 2021. [DOI: 10.1016/j.jmva.2021.104779] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Egashira K, Yata K, Aoshima M. Asymptotic properties of distance-weighted discrimination and its bias correction for high-dimension, low-sample-size data. JAPANESE JOURNAL OF STATISTICS AND DATA SCIENCE 2021. [DOI: 10.1007/s42081-021-00135-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Nakagawa T, Watanabe H, Hyodo M. Kick-one-out-based variable selection method for Euclidean distance-based classifier in high-dimensional settings. J MULTIVARIATE ANAL 2021. [DOI: 10.1016/j.jmva.2021.104756] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Nakayama Y. Robust support vector machine for high-dimensional imbalanced data. COMMUN STAT-SIMUL C 2021. [DOI: 10.1080/03610918.2019.1586922] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Affiliation(s)
- Yugo Nakayama
- Graduate School of Pure and Applied Sciences, University of Tsukuba, Tsukuba-shi, Ibaraki, Japan
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Chang W, Ahn J, Jung S. Double data piling leads to perfect classification. Electron J Stat 2021. [DOI: 10.1214/21-ejs1945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
- Woonyoung Chang
- Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA 15213, United States
| | - Jeongyoun Ahn
- Department of Industrial and Systems Engineering, KAIST, Daejeon 34141, South Korea
| | - Sungkyu Jung
- Department of Statistics, Seoul National University, Seoul 08826, South Korea
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Ishii A. A classifier under the strongly spiked eigenvalue model in high-dimension, low-sample-size context. COMMUN STAT-THEOR M 2020. [DOI: 10.1080/03610926.2018.1528365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Affiliation(s)
- Aki Ishii
- Department of Information Sciences, Tokyo University of Science, Chiba, Japan
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Hyodo M, Nishiyama T. Simultaneous testing of the mean vector and covariance matrix among k populations for high-dimensional data. COMMUN STAT-THEOR M 2019. [DOI: 10.1080/03610926.2019.1639751] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Affiliation(s)
- Masashi Hyodo
- Department of Mathematical Sciences, Graduate School of Engineering, Osaka Prefecture University, Naka-ku, Sakai, Osaka, Japan
| | - Takahiro Nishiyama
- Department of Business Administration, Senshu University, Tama-ku, Kawasaki-shi, Kanagawa, Japan
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Bias-corrected support vector machine with Gaussian kernel in high-dimension, low-sample-size settings. ANN I STAT MATH 2019. [DOI: 10.1007/s10463-019-00727-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Ishii A, Yata K, Aoshima M. Inference on high-dimensional mean vectors under the strongly spiked eigenvalue model. JAPANESE JOURNAL OF STATISTICS AND DATA SCIENCE 2018. [DOI: 10.1007/s42081-018-0029-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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