scholarly journals Bayes factor asymptotics for variable selection in the Gaussian process framework

2021 ◽  
Author(s):  
Minerva Mukhopadhyay ◽  
Sourabh Bhattacharya
Keyword(s):  
Variable Selection ◽  
Gaussian Process ◽  
Bayes Factor ◽  
Process Framework

scholarly journals Joint variable selection and network modeling for detecting eQTLs

2020 ◽  
Vol 19 (1) ◽  
Author(s):  
Xuan Cao ◽  
Lili Ding ◽  
Tesfaye B. Mersha
Keyword(s):  
Variable Selection ◽  
Multiple Testing ◽  
Bayes Factor ◽  
Graph Model ◽  
Real Data ◽  
Bayesian Regression ◽  
Joint Estimation ◽  
Eqtl Analysis ◽  
Multiple Testing Correction ◽  
Joint Variable

AbstractIn this study, we conduct a comparison of three most recent statistical methods for joint variable selection and covariance estimation with application of detecting expression quantitative trait loci (eQTL) and gene network estimation, and introduce a new hierarchical Bayesian method to be included in the comparison. Unlike the traditional univariate regression approach in eQTL, all four methods correlate phenotypes and genotypes by multivariate regression models that incorporate the dependence information among phenotypes, and use Bayesian multiplicity adjustment to avoid multiple testing burdens raised by traditional multiple testing correction methods. We presented the performance of three methods (MSSL – Multivariate Spike and Slab Lasso, SSUR – Sparse Seemingly Unrelated Bayesian Regression, and OBFBF – Objective Bayes Fractional Bayes Factor), along with the proposed, JDAG (Joint estimation via a Gaussian Directed Acyclic Graph model) method through simulation experiments, and publicly available HapMap real data, taking asthma as an example. Compared with existing methods, JDAG identified networks with higher sensitivity and specificity under row-wise sparse settings. JDAG requires less execution in small-to-moderate dimensions, but is not currently applicable to high dimensional data. The eQTL analysis in asthma data showed a number of known gene regulations such as STARD3, IKZF3 and PGAP3, all reported in asthma studies. The code of the proposed method is freely available at GitHub (https://github.com/xuan-cao/Joint-estimation-for-eQTL).

scholarly journals A Gaussian process framework for modelling instrumental systematics: application to transmission spectroscopy

2011 ◽  
Vol 419 (3) ◽  
pp. 2683-2694 ◽  
Author(s):  
N. P. Gibson ◽  
S. Aigrain ◽  
S. Roberts ◽  
T. M. Evans ◽  
M. Osborne ◽  
...  
Keyword(s):  
Gaussian Process ◽  
Transmission Spectroscopy ◽  
Process Framework

scholarly journals A Gaussian Process Framework for Overlap and Causal Effect Estimation with High-Dimensional Covariates

2019 ◽  
Vol 7 (2) ◽  
Author(s):  
Debashis Ghosh ◽  
Efrén Cruz Cortés
Keyword(s):  
Gaussian Process ◽  
Observational Studies ◽  
Reproducing Kernel ◽  
Causal Effect ◽  
Reproducing Kernel Hilbert Space ◽  
High Dimensional ◽  
Process Framework ◽  
Data Adaptive ◽  
Effect Estimation ◽  
Made In

AbstractA powerful tool for the analysis of nonrandomized observational studies has been the potential outcomes model. Utilization of this framework allows analysts to estimate average treatment effects. This article considers the situation in which high-dimensional covariates are present and revisits the standard assumptions made in causal inference. We show that by employing a flexible Gaussian process framework, the assumption of strict overlap leads to very restrictive assumptions about the distribution of covariates, results for which can be characterized using classical results from Gaussian random measures as well as reproducing kernel Hilbert space theory. In addition, we propose a strategy for data-adaptive causal effect estimation that does not rely on the strict overlap assumption. These findings reveal under a focused framework the stringency that accompanies the use of the treatment positivity assumption in high-dimensional settings.

scholarly journals Wireless Traffic Prediction With Scalable Gaussian Process: Framework, Algorithms, and Verification

2019 ◽  
Vol 37 (6) ◽  
pp. 1291-1306 ◽  
Author(s):  
Yue Xu ◽  
Feng Yin ◽  
Wenjun Xu ◽  
Jiaru Lin ◽  
Shuguang Cui
Keyword(s):  
Gaussian Process ◽  
Traffic Prediction ◽  
Process Framework

scholarly journals A Gaussian process framework for modelling stellar activity signals in radial velocity data

2015 ◽  
Vol 452 (3) ◽  
pp. 2269-2291 ◽  
Author(s):  
V. Rajpaul ◽  
S. Aigrain ◽  
M. A. Osborne ◽  
S. Reece ◽  
S. Roberts
Keyword(s):  
Radial Velocity ◽  
Gaussian Process ◽  
Velocity Data ◽  
Stellar Activity ◽  
Process Framework ◽  
Radial Velocity Data
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