Joint Diagnosis of High-Dimensional Process Mean and Covariance Matrix based on Bayesian Model Selection-Reference-Cited by-同舟云学术

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Joint Diagnosis of High-Dimensional Process Mean and Covariance Matrix based on Bayesian Model Selection

Published:2023-04-10 Issue:4 Volume:65 Page:465-479
ISSN:0040-1706
Container-title:Technometrics
language:en
Short-container-title:Technometrics

Author:

Xu Feng¹,Shu Lianjie²^ORCID,Li Yanting³,Binhui ,Wang ⁴

Affiliation:

1. College of Science, Guilin University of Technology, Guilin, China

2. Faculty of Business Administration, University of Macau, Taipa, Macau

3. Department of Industrial Engineering and Management, Shanghai Jiao Tong University, Shanghai, China

4. School of Management, Jinan University, Guangzhou, China

Funder

Guangxi Natural Science Foundation Program

Doctoral Research Foundation of Guilin University of Technology

Science and Technology Development Fund of Macau SAR

Department of Science and Technology of Guangdong Province

University of Macau Research Committee

Publisher

Informa UK Limited

Subject

Applied Mathematics,Modeling and Simulation,Statistics and Probability

Link

https://www.tandfonline.com/doi/pdf/10.1080/00401706.2023.2182366

Reference28 articles.

1. Criteria for Bayesian model choice with application to variable selection

2. Multivariate Bayesian variable selection and prediction

3. Consistency of Bayesian procedures for variable selection

4. On-line control of false discovery rates for multiple datastreams

5. Phase I analysis of high-dimensional covariance matrices based on sparse leading eigenvalues

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