scholarly journals Fast Bayesian variable selection for high dimensional linear models: Marginal solo spike and slab priors

2019 ◽  
Vol 13 (1) ◽  
pp. 284-309 ◽  
Author(s):  
Su Chen ◽  
Stephen G. Walker
Keyword(s):  
Variable Selection ◽  
Linear Models ◽  
Bayesian Variable Selection ◽  
High Dimensional ◽  
Selection For

Sparse Bayesian variable selection for classifying high-dimensional data

2018 ◽  
Vol 11 (2) ◽  
pp. 385-395 ◽  
Author(s):  
Aijun Yang ◽  
Heng Lian ◽  
Xuejun Jiang ◽  
Pengfei Liu
Keyword(s):  
Variable Selection ◽  
High Dimensional Data ◽  
Bayesian Variable Selection ◽  
High Dimensional ◽  
Selection For

scholarly journals Scalable Bayesian variable selection for structured high‐dimensional data

Biometrics ◽  
2018 ◽  
Vol 74 (4) ◽  
pp. 1372-1382 ◽  
Author(s):  
Changgee Chang ◽  
Suprateek Kundu ◽  
Qi Long
Keyword(s):  
Variable Selection ◽  
High Dimensional Data ◽  
Bayesian Variable Selection ◽  
High Dimensional ◽  
Selection For

Variable selection for high-dimensional quadratic Cox model with application to Alzheimer’s disease

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Cong Li ◽  
Jianguo Sun
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Variable Selection ◽  
Linear Models ◽  
Cox Model ◽  
Partial Likelihood ◽  
High Dimensional ◽  
Stat Assoc ◽  
Selection For ◽  
Disease Study

AbstractThis paper discusses variable or covariate selection for high-dimensional quadratic Cox model. Although many variable selection methods have been developed for standard Cox model or high-dimensional standard Cox model, most of them cannot be directly applied since they cannot take into account the important and existing hierarchical model structure. For the problem, we present a penalized log partial likelihood-based approach and in particular, generalize the regularization algorithm under marginality principle (RAMP) proposed in Hao et al. (J Am Stat Assoc 2018;113:615–25) under the context of linear models. An extensive simulation study is conducted and suggests that the presented method works well in practical situations. It is then applied to an Alzheimer’s Disease study that motivated this investigation.

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