scholarly journals Asymptotic Optimality of One-Group Shrinkage Priors in Sparse High-dimensional Problems

2017 ◽  
Vol 12 (4) ◽  
pp. 1133-1161 ◽  
Author(s):  
Prasenjit Ghosh ◽  
Arijit Chakrabarti
Keyword(s):  
Asymptotic Optimality ◽  
High Dimensional ◽  
Shrinkage Priors

MCMC for Global-Local Shrinkage Priors in High-Dimensional Settings

2021 ◽  
pp. 161-178
Author(s):  
Anirban Bhattacharya ◽  
James Johndrow
Keyword(s):  
High Dimensional ◽  
Shrinkage Priors

scholarly journals Model Averaging Estimation for High-dimensional Covariance Matrix with a Network Structure

2020 ◽  
Author(s):  
Rong Zhu ◽  
Xinyu Zhang ◽  
Yanyuan Ma ◽  
Guohua Zou
Keyword(s):  
Covariance Matrix ◽  
Network Structure ◽  
Averaging Method ◽  
Asymptotic Optimality ◽  
Model Averaging ◽  
High Dimensional ◽  
Type Model ◽  
Model Average ◽  
Squared Error

Abstract In this paper, we develop a model averaging method to estimate the high-dimensional covariance matrix, where the candidate models are constructed by different orders of the polynomial functions. We propose a Mallows-type model averaging criterion and select the weights by minimizing this criterion, which is an unbiased estimator of the expected in-sample squared error plus a constant. Then, we prove the asymptotic optimality of the resulting model average covariance (MAC) estimators. Furthermore, numerical simulations and a case study on Chinese airport network structure data are conducted to demonstrate the usefulness of the proposed approaches.

scholarly journals Bayesian Shrinkage Estimation of High Dimensional Causal Mediation Effects in Omics Studies

2018 ◽  
Author(s):  
Yanyi Song ◽  
Xiang Zhou ◽  
Min Zhang ◽  
Wei Zhao ◽  
Yongmei Liu ◽  
...  
Keyword(s):  
Mediation Analysis ◽  
Epidemiological Data ◽  
Joint Analysis ◽  
High Dimensional ◽  
Mediation Effects ◽  
Causal Mediation ◽  
Bayesian Inference Method ◽  
Bayesian Shrinkage ◽  
Causal Mediation Analysis ◽  
Shrinkage Priors

AbstractCausal mediation analysis aims to examine the role of a mediator or a group of mediators that lie in the pathway between an exposure and an outcome. Recent biomedical studies often involve a large number of potential mediators based on high-throughput technologies. Most of the current analytic methods focus on settings with one or a moderate number of potential mediators. With the expanding growth of omics data, joint analysis of molecular-level genomics data with epidemiological data through mediation analysis is becoming more common. However, such joint analysis requires methods that can simultaneously accommodate high-dimensional mediators and that are currently lacking. To address this problem, we develop a Bayesian inference method using continuous shrinkage priors to extend previous causal mediation analysis techniques to a high-dimensional setting. Simulations demonstrate that our method improves the power of global mediation analysis compared to simpler alternatives and has decent performance to identify true non-null mediators. We also construct tests for natural indirect effects using a permutation procedure. The Bayesian method helps us to understand the structure of the composite null hypotheses. We applied our method to Multi-Ethnic Study of Atherosclerosis (MESA) and identified DNA methylation regions that may actively mediate the effect of socioeconomic status (SES) on cardiometabolic outcome.

Sign in / Sign up

Export Citation Format

Share Document