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Bayesian Adaptive Lasso Quantile Regression [PDF]
Statistical Modelling, 2012 Recently, variable selection by penalized likelihood has attracted much research interest. In this paper, we propose adaptive Lasso quantile regression (BALQR) from a Bayesian perspective.
Keming Yu, Rahim Alhamzawi
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Bayesian Doubly Adaptive Elastic-Net Lasso for VAR Shrinkage
International Journal of Forecasting, 2014 We develop a novel Bayesian doubly adaptive elastic-net Lasso (DAELasso) approach for VAR shrinkage. DAELasso achieves variable selection and coefficient shrinkage in a data-based manner.
Gefang, Deborah
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The Bayesian Adaptive Lasso Regression
Mathematical Biosciences, 2018Classical adaptive lasso regression is known to possess the oracle properties; namely, it performs as well as if the correct submodel were known in advance. However, it requires consistent initial estimates of the regression coefficients, which are generally not available in high dimensional settings.
Haithem Taha Mohammad Ali +1 more
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Bayesian Bootstrap Adaptive Lasso Estimators of Regression Models
Journal of Statistical Computation and Simulation, 2021This paper proposes a modified adaptive lasso method by the Bayesian bootstrap (BBAL) and approximates the posterior distributions of parameters for a linear and a logistic regression model, respec...
Juan Wu
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Variational Inference on a Bayesian Adaptive Lasso Tobit Quantile Regression Model
Stat, 2023 The Tobit quantile regression model is a useful tool for quantifying the relationship between response variables with limited values and the explanatory variables. Under the Bayesian framework, the Tobit quantile regression model is often simulated by an asymmetric Laplacian distribution (ALD), which can be reformulated as a hierarchical structure ...
Ying Wu, Weili Cheng
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New Scale Mixture for Bayesian Adaptive Lasso Tobit Regression
Journal of Al-Rafidain University College For Sciences ( Print ISSN: 1681-6870 ,Online ISSN: 2790-2293 ), 2021Abbas [1] proposed new hierarchical representation of the adaptive Bayesian lasso model as uniform density , mixing with standard exponential distribution based on a transformation of the mixture of uniform density and a particular gamma distribution formulation provided by Mallick & Yi [2] They consider the new proposed hierarchical formulation ...
Ahmad Flaih +2 more
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Bayesian Adaptive Lasso for Quantile Regression Models With Nonignorably Missing Response Data
Communications in Statistics Part B: Simulation and Computation, 2019Handling data with the nonignorably missing mechanism is still a challenging problem in statistics.
Niansheng Tang
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Bayesian adaptive Lasso estimation of large graphical model based on modified Cholesky decomposition
Statistics & Probability Letters, 2023zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Fanqun Li, Mingtao Zhao, Kongsheng Zhang
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Structure Detection of Semiparametric Structural Equation Models With Bayesian Adaptive Group Lasso
Statistics in Medicine, 2015Structural equation models (SEMs) are widely recognized as the most important statistical tool for assessing the interrelationships among latent variables. This study develops a Bayesian adaptive group least absolute shrinkage and selection operator procedure to perform simultaneous model selection and estimation for semiparametric SEMs, wherein the ...
Xiangnan Feng, Xinyuan Song
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Bayesian adaptive lasso for additive hazard regression with current status data
Statistics in Medicine, 2019Variable selection is a crucial issue in model building and it has received considerable attention in the literature of survival analysis. However, available approaches in this direction have mainly focused on time‐to‐event data with right censoring. Moreover, a majority of existing variable selection procedures for survival models are developed in a ...
Chunjie Wang +3 more
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