Variable Selection Using aModified Gibbs Sampler Algorithm with Application on Rock Strength Dataset

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.

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Bayesian variable selection for latent class analysis using a collapsed Gibbs sampler

Statistics and Computing ◽

10.1007/s11222-014-9542-5 ◽

2014 ◽

Vol 26 (1-2) ◽

pp. 511-527 ◽

Cited By ~ 12

Author(s):

Arthur White ◽

Jason Wyse ◽

Thomas Brendan Murphy

Keyword(s):

Variable Selection ◽

Latent Class Analysis ◽

Gibbs Sampler ◽

Latent Class ◽

Bayesian Variable Selection ◽

Class Analysis ◽

Selection For

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Bayesian Variable Selection Using the Gibbs Sampler

Generalized Linear Models ◽

10.1201/9781482293456-25 ◽

2000 ◽

pp. 291-304

Keyword(s):

Variable Selection ◽

Gibbs Sampler ◽

Bayesian Variable Selection

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A fully Bayesian approach to sparse reduced-rank multivariate regression

Statistical Modelling ◽

10.1177/1471082x20948697 ◽

2020 ◽

pp. 1471082X2094869

Author(s):

Dunfu Yang ◽

Gyuhyeong Goh ◽

Haiyan Wang

Keyword(s):

Variable Selection ◽

Bayesian Approach ◽

Gibbs Sampler ◽

Real Data ◽

Bayesian Framework ◽

Low Rank ◽

Mcmc Methods ◽

Reduced Rank Regression ◽

Reduced Rank ◽

Fully Bayesian

In the context of high-dimensional multivariate linear regression, sparse reduced-rank regression (SRRR) provides a way to handle both variable selection and low-rank estimation problems. Although there has been extensive research on SRRR, statistical inference procedures that deal with the uncertainty due to variable selection and rank reduction are still limited. To fill this research gap, we develop a fully Bayesian approach to SRRR. A major difficulty that occurs in a fully Bayesian framework is that the dimension of parameter space varies with the selected variables and the reduced-rank. Due to the varying-dimensional problems, traditional Markov chain Monte Carlo (MCMC) methods such as Gibbs sampler and Metropolis-Hastings algorithm are inapplicable in our Bayesian framework. To address this issue, we propose a new posterior computation procedure based on the Laplace approximation within the collapsed Gibbs sampler. A key feature of our fully Bayesian method is that the model uncertainty is automatically integrated out by the proposed MCMC computation. The proposed method is examined via simulation study and real data analysis.

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Polarity and Variable Selection Heuristics for SAT-Based Anytime MaxSAT

Journal on Satisfiability, Boolean Modeling and Computation ◽

10.3233/sat-200126 ◽

2020 ◽

Vol 12 (1) ◽

pp. 17-22

Author(s):

Alexander Nadel

Keyword(s):

Variable Selection ◽

Open Source ◽

System Description

This paper is a system description of the anytime MaxSAT solver TT-Open-WBO-Inc, which won both of the weighted incomplete tracks of MaxSAT Evaluation 2019. We implemented the recently introduced polarity and variable selection heuristics, TORC and TSB, respectively, in the Open-WBO-Inc-BMO algorithm within the open-source anytime MaxSAT solver Open-WBO-Inc. As a result, the solver is substantially more efficient.

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