Sparse Bayesian variable selection in multinomial probit regression model with application to high-dimensional data classification-Reference-Cited by-同舟云学术

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Sparse Bayesian variable selection in multinomial probit regression model with application to high-dimensional data classification

Published:2016-06-17 Issue:12 Volume:46 Page:6137-6150
ISSN:0361-0926
Container-title:Communications in Statistics - Theory and Methods
language:en
Short-container-title:Communications in Statistics - Theory and Methods

Author:

Aijun Yang¹²,Xuejun Jiang³,Liming Xiang⁴,Jinguan Lin⁵

Affiliation:

1. 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. School of Physical & Mathematical Sciences, Nanyang Technological University, Singapore

5. Department of Mathematics, Southeast University, Nanjing, China

Publisher

Informa UK Limited

Subject

Statistics and Probability

Link

https://www.tandfonline.com/doi/pdf/10.1080/03610926.2015.1122056

Reference39 articles.

1. Bayesian Analysis of Binary and Polychotomous Response Data

2. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling

3. Gene selection using a two-level hierarchical Bayesian model

4. Tissue Classification with Gene Expression Profiles

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