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Title: High dimensional discrimination analysis via a semiparametric model
Authors: Jiang, B 
Leng, C
Issue Date: Mar-2016
Source: Statistics and probability letters, Mar. 2016, v. 110, p. 103-110
Abstract: We propose a semiparametric linear programming discriminant (SLPD) rule for high dimensional discriminant analysis under a semiparametric model. As an extension, we further propose a two-stage SLPD (TSLPD) rule, which can have better classification performance under mild sparsity assumptions.
Keywords: Bayes rule
Linear discrimination analysis
Monotone transformation
Semiparametric discriminant analysis
Sparsity
Publisher: Elsevier
Journal: Statistics and probability letters 
ISSN: 0167-7152
DOI: 10.1016/j.spl.2015.11.012
Rights: © 2015 Elsevier B.V. All rights reserved.
© 2015. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/
The following publication Jiang, B., & Leng, C. (2016). High dimensional discrimination analysis via a semiparametric model. Statistics & Probability Letters, 110, 103-110 is available at https://doi.org/10.1016/j.spl.2015.11.012
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