An estimator of misclassification probability for multi-class Euclidean distance classifier in high-dimensional data-Reference-Cited by-同舟云学术

An estimator of misclassification probability for multi-class Euclidean distance classifier in high-dimensional data

Published:2019-06-01 Issue:1 Volume:55 Page:
ISSN:0916-5746
Container-title:SUT Journal of Mathematics
language:
Short-container-title:SUT J. Math.

Author:

Watanabe Hiroki¹,Seo Takashi²,Hyodo Masashi³

Affiliation:

1. Department of Health Sciences, Oita University of Nursing and Health Sciences 2944-9, Megusuno, Oita-shi, Oita 870-1201, Japan

2. Department of Applied Mathematics, Tokyo University of Science 1-3 Kagurazaka, Shinjuku-ku, Tokyo 162-8601, Japan

3. Department of Mathematical Sciences, Graduate School of Science Osaka Prefecture University, 1-1 Gakuen-cho, Naka-ku, Sakai-shi, Osaka 599-8531, Japan

Publisher

SUT Journal of Mathematics - Tokyo University of Science

Subject

General Mathematics

Reference14 articles.

1. [1] Aoshima, M. and Yata, K. (2014). A distance-based, misclassification rate adjusted classifier for multiclass, high-dimensional data. Ann. Inst. Stat. Math., 66, 983–1010.

2. [2] Aoshima, M. and Yata, K. (2019a). Distance-based classifier by data transformation for high-dimension, strongly spiked eigenvalue models. Ann. Inst. Stat. Math., 71, 473–503.

3. [3] Aoshima, M. and Yata, K. (2019b). High-dimensional quadratic classifiers in non-sparse settings. Methodol Comput. Appl. Probab., 21, 663–682.

4. [4] Cramér, H. and Wold, H. (1936). Some theorems on distribution functions. J. London Math. Soc., s1-11, 290–294.

5. [5] Fujikoshi, Y. and Seo, T. (1998). Asymptotic approximations for EPMC’s of the linear and the quadratic discriminant functions when the sample sizes and the dimension are large. Random Operators and Stochastic Equations, 6, 269–280.