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

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Ghadeer J. M. Mahdi
https://orcid.org/0000-0003-4870-4034
Othman M. Salih
https://orcid.org/0000-0002-9908-8748

Abstract

Variable selection is an essential and necessary task in the statistical modeling field. Several studies have triedto develop and standardize the process of variable selection, but it isdifficultto do so. The first question a researcher needs to ask himself/herself what are the most significant variables that should be used to describe a given dataset’s response. In thispaper, a new method for variable selection using Gibbs sampler techniqueshas beendeveloped.First, the model is defined, and the posterior distributions for all the parameters are derived.The new variable selection methodis tested usingfour simulation datasets. The new approachiscompared with some existingtechniques: Ordinary Least Squared (OLS), Least Absolute Shrinkage and Selection Operator (Lasso), and Tikhonov Regularization (Ridge). The simulation studiesshow that the performance of our method is better than the othersaccording to the error and the time complexity. Thesemethodsare applied to a real dataset, which is called Rock StrengthDataset.The new approach implemented using the Gibbs sampler is more powerful and effective than other approaches.All the statistical computations conducted for this paper are done using R version 4.0.3 on a single processor computer.

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Variable Selection Using aModified Gibbs Sampler Algorithm with Application on Rock Strength Dataset. Baghdad Sci.J [Internet]. 2022 Jun. 1 [cited 2024 Nov. 19];19(3):0551. Available from: https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/5159
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How to Cite

1.
Variable Selection Using aModified Gibbs Sampler Algorithm with Application on Rock Strength Dataset. Baghdad Sci.J [Internet]. 2022 Jun. 1 [cited 2024 Nov. 19];19(3):0551. Available from: https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/5159

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