scholarly journals Bayesian adaptive Lasso Tobit regression

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Haider Kadhim Abbas ◽  
Rahim Jabbar Thaher
Keyword(s):  
Data Analysis ◽  
Model Selection ◽  
Hierarchical Model ◽  
Gibbs Sampler ◽  
Real Data ◽  
Bayesian Hierarchical Model ◽  
Adaptive Lasso ◽  
Tobit Regression ◽  
Bayesian Hierarchical ◽  
Coefficient Estimation

      In this paper, we introduce a new procedure for model selection in Tobit regression, we suggest the Bayesian adaptive Lasso Tobit regression (BALTR) for variable selection (VS) and coefficient estimation.  We submitted a Bayesian hierarchical model and Gibbs sampler (GS) for our procedure. Our proposed procedure is clarified by means of simulations and a real data analysis. Results demonstrate our procedure performs well in comparison to further procedures.  

scholarly journals Robust Bayesian Regularized Estimation Based ontRegression Model

2015 ◽  
Vol 2015 ◽  
pp. 1-9
Author(s):  
Zean Li ◽  
Weihua Zhao
Keyword(s):  
Gibbs Sampler ◽  
Heavy Tails ◽  
Real Data ◽  
Adaptive Lasso ◽  
Data Sets ◽  
Simulation Studies ◽  
Model Framework ◽  
Bayesian Hierarchical ◽  
Coefficient Estimation ◽  
Gamma Distributions

Thetdistribution is a useful extension of the normal distribution, which can be used for statistical modeling of data sets with heavy tails, and provides robust estimation. In this paper, in view of the advantages of Bayesian analysis, we propose a new robust coefficient estimation and variable selection method based on Bayesian adaptive Lassotregression. A Gibbs sampler is developed based on the Bayesian hierarchical model framework, where we treat thetdistribution as a mixture of normal and gamma distributions and put different penalization parameters for different regression coefficients. We also consider the Bayesiantregression with adaptive group Lasso and obtain the Gibbs sampler from the posterior distributions. Both simulation studies and real data example show that our method performs well compared with other existing methods when the error distribution has heavy tails and/or outliers.

scholarly journals BAGSE: a Bayesian hierarchical model approach for gene set enrichment analysis

2019 ◽  
Author(s):  
Abhay Hukku ◽  
Corbin Quick ◽  
Francesca Luca ◽  
Roger Pique-Regi ◽  
Xiaoquan Wen
Keyword(s):  
Hierarchical Model ◽  
Enrichment Analysis ◽  
Real Data ◽  
Bayesian Hierarchical Model ◽  
Gene Set Enrichment Analysis ◽  
Simulation Studies ◽  
Gene Set Enrichment ◽  
Bayesian Hierarchical ◽  
Gene Set ◽  
Causal Pathways

AbstractGene set enrichment analysis has been shown to be effective in identifying relevant biological pathways underlying complex diseases. Existing approaches lack the ability to quantify the enrichment levels accurately, hence preventing the enrichment information to be further utilized in both upstream and downstream analyses. A modernized and rigorous approach for gene set enrichment analysis that emphasizes both hypothesis testing and enrichment estimation is much needed. We propose a novel computational method, Bayesian Analysis of Gene Set Enrichment (BAGSE), for gene set enrichment analysis. BAGSE is built on a Bayesian hierarchical model and fully accounts for the uncertainty embedded in the association evidence of individual genes. We adopt an empirical Bayes inference framework to fit the proposed hierarchical model by implementing an efficient EM algorithm. Through simulation studies, we illustrate that BAGSE yields accurate enrichment quantification while achieving similar power as the state-of-the-art methods. Further simulation studies show that BAGSE can effectively utilize the enrichment information to improve the power in gene discovery. Finally, we demonstrate the application of BAGSE in analyzing real data from a differential expression experiment and a Transcriptome-wide Association Study (TWAS). Our results indicate that the proposed statistical framework is effective in aiding the discovery of potentially causal pathways and gene networks. BAGSE is implemented using the C++ programming language and is freely available from https://github.com/xqwen/bagse/. Simulated and real data used in this paper are also available at the Github repository for reproducibility purposes.

scholarly journals Bayesian Lasso Tobit regression

2019 ◽  
Vol 11 (2) ◽  
pp. 1-13 ◽  
Author(s):  
Haider Kadhim Abbas
Keyword(s):  
Model Selection ◽  
Variable Selection ◽  
Covariance Matrix ◽  
Gibbs Sampler ◽  
Real Data ◽  
New Technique ◽  
Tobit Regression ◽  
Bayesian Lasso ◽  
New Approach ◽  
Selection Property

In the present research, we have proposed a new approach for model selection in Tobit regression. The new technique uses Bayesian Lasso in Tobit regression (BLTR). It has many features that give optimum estimation and variable selection property. Specifically, we introduced a new hierarchal model. Then, a new Gibbs sampler is introduced.We also extend the new approach by adding the ridge parameter inside the variance covariance matrix to avoid the singularity in the case of multicollinearity or in case the number of predictors greater than the number of observations. A comparison was made with other previous techniques applying the simulation examples and real data. It is worth mentioning, that the obtained results were promising and encouraging, giving better results compared to the previous methods.

scholarly journals BAGSE: a Bayesian hierarchical model approach for gene set enrichment analysis

2019 ◽  
Vol 36 (6) ◽  
pp. 1689-1695 ◽  
Author(s):  
Abhay Hukku ◽  
Corbin Quick ◽  
Francesca Luca ◽  
Roger Pique-Regi ◽  
Xiaoquan Wen
Keyword(s):  
Hierarchical Model ◽  
Enrichment Analysis ◽  
Real Data ◽  
Bayesian Hierarchical Model ◽  
Gene Set Enrichment Analysis ◽  
Supplementary Information ◽  
Simulation Studies ◽  
Gene Set Enrichment ◽  
Bayesian Hierarchical ◽  
Gene Set

Abstract Motivation Gene set enrichment analysis has been shown to be effective in identifying relevant biological pathways underlying complex diseases. Existing approaches lack the ability to quantify the enrichment levels accurately, hence preventing the enrichment information to be further utilized in both upstream and downstream analyses. A modernized and rigorous approach for gene set enrichment analysis that emphasizes both hypothesis testing and enrichment estimation is much needed. Results We propose a novel computational method, Bayesian Analysis of Gene Set Enrichment (BAGSE), for gene set enrichment analysis. BAGSE is built on a Bayesian hierarchical model and fully accounts for the uncertainty embedded in the association evidence of individual genes. We adopt an empirical Bayes inference framework to fit the proposed hierarchical model by implementing an efficient EM algorithm. Through simulation studies, we illustrate that BAGSE yields accurate enrichment quantification while achieving similar power as the state-of-the-art methods. Further simulation studies show that BAGSE can effectively utilize the enrichment information to improve the power in gene discovery. Finally, we demonstrate the application of BAGSE in analyzing real data from a differential expression experiment and a transcriptome-wide association study. Our results indicate that the proposed statistical framework is effective in aiding the discovery of potentially causal pathways and gene networks. Availability and implementation BAGSE is implemented using the C++ programing language and is freely available from https://github.com/xqwen/bagse/. Simulated and real data used in this paper are also available at the Github repository for reproducibility purposes. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals A New Bayesian Group Bridge to Solve the Tobit Model

2020 ◽  
pp. 215-222
Author(s):  
Saja Aljanabi ◽  
Rahim Alhamzawi
Keyword(s):  
Censored Data ◽  
Hierarchical Model ◽  
Gibbs Sampler ◽  
Bayesian Hierarchical Model ◽  
Tobit Model ◽  
Censored Regression ◽  
New Approach ◽  
Bayesian Hierarchical ◽  
Posterior Sampling

In this paper, we propose a new approach of regularization for the left censored data (Tobit). Specifically, we propose a new Bayesian group Bridge for left-censored regression ( BGBRLC). We developed a new Bayesian hierarchical model and we suggest a new Gibbs sampler for posterior sampling. The results show that the new approach performs very well compared to some existing approaches.

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