Bayesian group bridge composite quantile regression

Bibliographic Data

ID12951408
AuthorsMayyadah Aljasimee (University of Al-Qadisiyah, corresponding author), Rahim Alhamzawi (0000-0003-1912-9579, University of Al-Qadisiyah)
Year2023
Volume33
Publication date2023-03-10
Open AccessNo
TypeARTICLE
VenueJournal of Namibian Studies History Politics Culture (JOURNAL)
Journal identifiersISSN: 1863-5954 • E-ISSN: 2197-5523
PublisherDewspublication (PUBLISHER)
DOI10.59670/jns.v33i.516
OpenAlexW4381197301
LanguageEN
References cited5

Bayesian regularized composite quantile regression (CQR) method with group bridge penalty is adopted to conduct covariate selection and estimation in CQR. MCMC algorithm was improved for posterior inference employing a scale mixture of normal of the asymmetric Laplace distribution (ALD). The suggested algorithm uses priors for the coefficients of regression, which are scale mixtures of multivariate uniform distributions with a particular Gamma distribution as a mixing distribution. Simulation results and analyses of real data show that the suggested MCMC sampler has excellent mixing feature and outperforms the current approaches in terms of prediction accuracy and model selection

Bayesian hierarchical modeling · Bayesian inference · Bayesian linear regression · Bayesian probability · Covariate · Feature selection · Markov chain Monte Carlo · Model selection · Prior probability · Quantile · Quantile regression · Statistics · Advanced Statistical Methods and Models · Bayesian Methods and Mixture Models · Computer Science · Grey System Theory Applications · Mathematics · Artificial Intelligence

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