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Bayesian variable selection with sparse and correlation priors for high-dimensional data analysis
Aijun Yang1,2; Xuejun Jiang3; Lianjie Shu4; Jinguan Lin5
2017-03
Source PublicationCOMPUTATIONAL STATISTICS
ISSN0943-4062
Volume32Issue:1Pages:127-143
Abstract

The main challenge in working with gene expression microarrays is that the sample size is small compared to the large number of variables (genes). In many studies, the main focus is on finding a small subset of the genes, which are the most important ones for differentiating between different types of cancer, for simpler and cheaper diagnostic arrays. In this paper, a sparse Bayesian variable selection method in probit model is proposed for gene selection and classification. We assign a sparse prior for regression parameters and perform variable selection by indexing the covariates of the model with a binary vector. The correlation prior for the binary vector assigned in this paper is able to distinguish models with the same size. The performance of the proposed method is demonstrated with one simulated data and two well known real data sets, and the results show that our method is comparable with other existing methods in variable selection and classification.

KeywordBayesian Variable Selection Sparse Prior Correlation Prior Probit Model High-dimensional Data Classification
DOI10.1007/s00180-016-0665-3
URLView the original
Indexed BySCI
Language英语
WOS Research AreaMathematics
WOS SubjectStatistics & Probability
WOS IDWOS:000392300200006
PublisherSPRINGER HEIDELBERG
The Source to ArticleWOS
Fulltext Access
Citation statistics
Cited Times [WOS]:2   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF ACCOUNTING AND INFORMATION MANAGEMENT
Corresponding AuthorXuejun Jiang
Affiliation1.College of Economics and Management, Nanjing Forestry University, Nanjing, China
2.School of Economics and Management, Southeast University, Nanjing, China
3.Department of Mathematics, South University of Science and Technology of China, Shenzhen, China
4.Faculty of Business Administration, University of Macau, Macau, China
5.Department of Mathematics, Southeast University, Nanjing, China
Recommended Citation
GB/T 7714
Aijun Yang,Xuejun Jiang,Lianjie Shu,et al. Bayesian variable selection with sparse and correlation priors for high-dimensional data analysis[J]. COMPUTATIONAL STATISTICS,2017,32(1):127-143.
APA Aijun Yang,Xuejun Jiang,Lianjie Shu,&Jinguan Lin.(2017).Bayesian variable selection with sparse and correlation priors for high-dimensional data analysis.COMPUTATIONAL STATISTICS,32(1),127-143.
MLA Aijun Yang,et al."Bayesian variable selection with sparse and correlation priors for high-dimensional data analysis".COMPUTATIONAL STATISTICS 32.1(2017):127-143.
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