Results 11 to 20 of about 2,889,354 (160)
Environment and Natural Resources Journal, 2022 A Bayesian approach was used to develop binary quantile regression models featuring the lasso penalty. The models afford the advantages of all quantile regression models, such as robustness and detailed insights into covariate effects; they also handle ...
Paanwaris Paansri +4 more
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BAYESIAN ANALYSIS OF TOBIT QUANTILE REGRESSION WITH ADAPTIVE LASSO PENALTY IN HOUSEHOLD EXPENDITURE FOR CIGARETTE CONSUMPTION [PDF]
Jurnal Statistika Universitas Muhammadiyah Semarang, 2023 Tobit Quantile Regression with Adaptive Lasso Penalty is a quantile regression model on censored data that adds Lasso's adaptive penalty to its parameter estimation. The estimation of the regression parameters is solved by Bayesian analysis.
Rahmawati, Fitri, Subanar, Subanar
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Identify Relative importance of covariates in Bayesian lasso quantile regression via new algorithm in statistical program R [PDF]
Revista Română de Statistică, 2017 In this paper, we propose a new algorithm to determine the relative importance of covariates by Bayesian Lasso quantile regression for variable selection assigning new formula of Laplace distributions for the regression parameters.
Fadel Hamid Hadi Alhusseini +2 more
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Prior elicitation and variable selection for bayesian quantile regression [PDF]
, 2013 This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Bayesian subset selection suffers from three important difficulties: assigning priors over model space, assigning priors to all components of the regression
Al-Hamzawi, Rahim Jabbar Thaher
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Regularized and robust regression methods for high dimensional data [PDF]
, 2014 This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University London.Recently, variable selection in high-dimensional data has attracted much research interest.
Hashem, Hussein Abdulahman
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Penalized Cumulative Probability Model for a Continuous Outcome Subject to Detection Limits. [PDF]
Stat MedABSTRACT Mixed‐type outcome data occur when the outcome variable's distribution is a mixture of both continuous and discrete ordinal variables. Such mixed‐type outcomes are common in biomedical, psychological, and the health sciences, particularly for variables having either a detection or quantitation limit.
Sun S, Mas VR, Archer KJ.
europepmc +2 more sources
A partially collapsed Gibbs sampler for Bayesian quantile regression [PDF]
, 2009 We introduce a set of new Gibbs sampler for Bayesian analysis of quantile re-gression model. The new algorithm, which partially collapsing an ordinary Gibbs sampler, is called Partially Collapsed Gibbs (PCG) sampler.
Yu, K, Reed, C
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High-Dimensional Variable Selection for Quantile Regression Based on Variational Bayesian Method
Mathematics, 2023 The quantile regression model is widely used in variable relationship research of moderate sized data, due to its strong robustness and more comprehensive description of response variable characteristics.
Dengluan Dai, Anmin Tang, Jinli Ye
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A Generalization of LASSO Modeling via Bayesian Interpretation
Austrian Journal of Statistics, 2023 The aim of this paper is to introduce a generalized LASSO regression model that is derived using a generalized Laplace (GL) distribution. Five different GL distributions are obtained through the T -R{Y } framework with quantile functions of standard ...
Gayan Warahena-Liyanage +2 more
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Bayesian Lasso-mixed quantile regression [PDF]
Journal of Statistical Computation and Simulation, 2012 In this paper, we discuss the regularization in linear-mixed quantile regression. A hierarchical Bayesian model is used to shrink the fixed and random effects towards the common population values by introducing an l1 penalty in the mixed quantile regression check function.
Alhamzawi, R, Yu, K
openaire +2 more sources