New hard-thresholding rules based on data splitting in high-dimensional imbalanced classification-Reference-Cited by-同舟云学术

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New hard-thresholding rules based on data splitting in high-dimensional imbalanced classification

Published:2022-01-01 Issue:1 Volume:16 Page:
ISSN:1935-7524
Container-title:Electronic Journal of Statistics
language:
Short-container-title:Electron. J. Statist.

Author:

Mojiri Arezou¹,Khalili Abbas²,Zeinal Hamadani Ali³

Affiliation:

1. Department of Mathematical Sciences, Isfahan University of Technology, Isfahan, 19395-5746, Iran

2. Department of Mathematics and Statistics, McGill University, Montreal, H3A 0B9, Canada

3. Department of Industrial and Systems Engineering, Isfahan University of Technology, Isfahan, 19395-5746, Iran

Publisher

Institute of Mathematical Statistics

Subject

Statistics and Probability,Statistics, Probability and Uncertainty

Reference45 articles.

1. Ahn, J. and J. Marron (2010). The maximal data piling direction for discrimination. Biometrika 97, 254–259.

2. Bach, M., A. Werner, J. Żywiec, and W. Pluskiewicz (2017). The study of under-and over-sampling methods’ utility in analysis of highly imbalanced data on osteoporosis. Information Sciences 384, 174–190.

3. Bak, B. A. and J. L. Jensen (2016). High dimensional classifiers in the imbalanced case. Computational Statistics & Data Analysis 98, 46–59.

4. Bickel, P. J. and E. Levina (2004). Some theory for Fisher’s linear discriminant function, naive Bayes’, and some alternatives when there are many more variables than observations. Bernoulli 10, 989–1010.

5. Bickel, P. J. and E. Levina (2008a). Covariance regularization by thresholding. Annals of Statistics 36, 2577–2604.

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