Fully Bayesian logistic regression with hyper-LASSO priors for high-dimensional feature selection
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Publication:4960726
DOI10.1080/00949655.2018.1490418OpenAlexW1592324551MaRDI QIDQ4960726
Publication date: 23 April 2020
Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1405.3319
horseshoeGibbs samplingMCMCfeature selectionhigh-dimensionalHamiltonian Monte Carlofully Bayesianheavy-tailed priornon-convex penaltieshyper-Lasso priors
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Cites Work
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