Acceleration of the stochastic search variable selection via componentwise Gibbs sampling

DOI10.1007/S00184-016-0604-XzbMath1367.62212OpenAlexW2554581679MaRDI QIDQ523262

Min-Qian Liu, Hengzhen Huang, Zong-Feng Qi, Shuang-Shuang Zhou

Publication date: 20 April 2017

Published in: Metrika (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00184-016-0604-x

zbMATH Keywords

Gibbs samplerlinear regressionBayesian variable selectionstochastic search variable selectionsupersaturated design

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Bayesian inference (62F15) Factorial statistical designs (62K15)

Cites Work

• Construction of supersaturated design with large number of factors by the complementary design method
• A two-stage variable selection strategy for supersaturated designs with multiple responses
• Supersaturated designs: a review of their construction and analysis
• Construction of equidistant and weak equidistant supersaturated designs
• On construction of optimal mixed-level supersaturated designs
• Construction of optimal supersaturated design with large number of levels
• Stochastic matching pursuit for Bayesian variable selection
• Analysis of supersaturated designs via the Dantzig selector
• Functionally induced priors for componentwise Gibbs sampler in the analysis of supersaturated designs
• A method for screening active effects in supersaturated designs
• The Calculation of Posterior Distributions by Data Augmentation
• A Bayesian Variable-Selection Approach for Analyzing Designed Experiments with Complex Aliasing
• Mathematical Statistics
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