Sparse Bayesian multinomial probit regression model with correlation prior for high-dimensional data classification

被引：0
|
作者：
Yang Aijun [1 ,2 ]
Jiang Xuejun [3 ]
Liu Pengfei [4 ]
Lin Jinguan [5 ]
机构：
[1] Nanjing Forestry Univ, Coll Econ & Management, Nanjing, Jiangsu, Peoples R China
[2] Southeast Univ, Sch Econ & Management, Nanjing, Jiangsu, Peoples R China
[3] South Univ Sci & Technol China, Dept Math, Shenzhen, Peoples R China
[4] Univ Jiangsu Normal Univ, Sch Math & Stat, Xuzhou, Peoples R China
[5] Nanjing Audit Univ, Inst Stat & Big Data, Nanjing, Jiangsu, Peoples R China
来源：
STATISTICS & PROBABILITY LETTERS | 2016年 / 119卷
基金：
中国博士后科学基金;
关键词：
Sparse Bayesian method; Multinomial probit model; Correlation prior; High-dimensional data classification; VARIABLE SELECTION; GENE SELECTION; CANCER;
D O I：
10.1016/j.spl.2016.08.008
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
Selecting a small number of relevant genes for cancer classification has received a great deal of attention in microarray data analysis. In this paper, a sparse Bayesian multinomial probit regression model with correlation prior is proposed. Based on simulated and real datasets, we demonstrate that the proposed method performs better than five other competing methods in terms of variable selection and classification. (C) 2016 Elsevier B.V. All rights reserved.
引用
收藏
页码：241 / 247
页数：7
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