Shrinkage Priors for High-Dimensional Demand Estimation

2021 ◽  
Author(s):  
Adam N. Smith ◽  
Jim E. Griffin
Keyword(s):  
Demand Estimation ◽  
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

Demand Estimation with High-Dimensional Product Characteristics

2015 ◽  
Author(s):  
Benjamin J. Gillen ◽  
Matthew Shum ◽  
Hyungsik Roger Moon
Keyword(s):  
Demand Estimation ◽  
High Dimensional ◽  
Product Characteristics

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

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.

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