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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. The method extends the Bayesian Lasso quantile regression by allowing different penalization parameters for different regression coefficients.
R. ALHAMZAWI, K. YU, D. F. BENOIT
exaly   +8 more sources

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 ...
Zhiqiang Wang, Ying Wu, WeiLi Cheng
openaire   +4 more sources

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. Parameters are assumed to follow a certain distribution called the prior distribution.
Fitri Rahmawati, Subanar Subanar
openaire   +2 more sources

Bayesian variable selection in linear quantile mixed models for longitudinal data with application to macular degeneration.

PLoS ONE, 2020
This paper presents a Bayesian analysis of linear mixed models for quantile regression based on a Cholesky decomposition for the covariance matrix of random effects.
Yonggang Ji, Haifang Shi
doaj   +2 more sources

Modified Quantile Regression for Modeling the Low Birth Weight

Frontiers in Applied Mathematics and Statistics, 2022
This study aims to identify the best model of low birth weight by applying and comparing several methods based on the quantile regression method's modification.
Ferra Yanuar, Hazmira Yozza, Aidinil Zetra   +2 more
doaj   +1 more source

Bayesian binary quantile regression adaptive Lasso for modelling hypertension: a bootstrap based consistency analysis

Communications in Statistics: Case Studies, Data Analysis and Applications
Lilis Harianti Hasibuan, Ferra Yanuar, Dodi Devianto, null Maiyastri   +3 more
openaire   +3 more sources

bayesQR: A Bayesian Approach to Quantile Regression

Journal of Statistical Software, 2017
After its introduction by Koenker and Basset (1978), quantile regression has become an important and popular tool to investigate the conditional response distribution in regression. The R package bayesQR contains a number of routines to estimate quantile
Dries F. Benoit, Dirk Van den Poel
doaj   +1 more source

Explanation of somatic symptoms by mental health and personality traits: application of Bayesian regularized quantile regression in a large population study

BMC Psychiatry, 2019
Background Somatic syndrome is one of the remarkably prevalent issues in primary health care and subspecialty settings. We aimed to elucidate multidimensional associations between somatic symptoms with major mental problems and personality traits in the ...
Shayan Mostafaei, Kourosh Kabir, Anoshirvan Kazemnejad, Awat Feizi, Marjan Mansourian, Ammar Hassanzadeh Keshteli, Hamid Afshar, Saeed Masoud Arzaghi, Saeid Rasekhi Dehkordi, Peyman Adibi, Fataneh Ghadirian   +10 more
doaj   +1 more source

Growing-season NDVI responses to climate change in China’s three major marsh wetland regions

Ecological Indicators
The dynamics of marsh wetland vegetation (MWV) play a vital role in the carbon cycle of marsh wetlands under the background global climate change. However, it is still unclear the response of MWV to climate change in China.
Yue Deng, Shujing Gao, Lulu Liu, Weiguo Jiang   +3 more
doaj   +1 more source

Adaptive Bayesian Quantile Regression for Count Data: A Random-Bridge Penalty Approach With Applications

Journal of Mathematics
A Bayesian modeling framework with a random-bridge prior is proposed for quantile regression of high-dimensional count data, addressing challenges in variable sparsity identification and model adaptability.
Youxi Luo, Ziqi Zhou, Chaozhu Hu, Hanfang Li   +3 more
doaj   +1 more source