Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/44044
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 |
Appears in Collections: | Journal/Magazine Article |
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Jiang_High_Dimensional_Discrimination.pdf | Pre-Published version | 1.02 MB | Adobe PDF | View/Open |
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