Bayesian Sparse Regression for Mixed Multi-Responses with Application to Runtime Metrics Prediction in Fog Manufacturing-Reference-Cited by-同舟云学术

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Bayesian Sparse Regression for Mixed Multi-Responses with Application to Runtime Metrics Prediction in Fog Manufacturing

Published:2022-10-31 Issue:2 Volume:65 Page:206-219
ISSN:0040-1706
Container-title:Technometrics
language:en
Short-container-title:Technometrics

Author:

Chen Xiaoyu¹^ORCID,Kang Xiaoning²^ORCID,Jin Ran³,Deng Xinwei⁴^ORCID

Affiliation:

1. Department of Industrial Engineering, University of Louisville, Louisville, KY

2. International Business College and Institute of Supply Chain Analytics, Dongbei University of Finance and Economics, Dalian, China

3. Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA

4. Department of Statistics, Virginia Tech, Blacksburg, VA

Publisher

Informa UK Limited

Subject

Applied Mathematics,Modeling and Simulation,Statistics and Probability

Link

https://www.tandfonline.com/doi/pdf/10.1080/00401706.2022.2134928

Reference32 articles.

1. Joint Bayesian Analysis of Multiple Response-Types Using the Hierarchical Generalized Transformation Model

2. AdaPipe: A Recommender System for Adaptive Computation Pipelines in Cyber-Manufacturing Computation Services

3. Predictive offloading in mobile-fog-cloud enabled cyber-manufacturing systems

4. Response models for mixed binary and quantitative variables

5. QQ Models: Joint Modeling for Quantitative and Qualitative Quality Responses in Manufacturing Systems

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1. Developing a deep learning-based uncertainty-aware tool wear prediction method using smartphone sensors for the turning process of Ti-6Al-4V;Journal of Manufacturing Systems;2024-10

2. American society of anesthesiologists physical status classification significantly affects the performances of machine learning models in intraoperative hypotension inference;Journal of Clinical Anesthesia;2024-02

3. A latent process approach to change-point detection of mixed-type observations;Quality Engineering;2023-06-28

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