Results 31 to 40 of about 113,685 (321)
Bayesian composite quantile regression for the single-index model.
PLoS ONE, 2023 By using a Gaussian process prior and a location-scale mixture representation of the asymmetric Laplace distribution, we develop a Bayesian analysis for the composite quantile single-index regression model.
Xiaohui Yuan, Xuefei Xiang, Xinran Zhang
doaj +1 more source
A Bayesian hurdle quantile regression model for citation analysis with mass points at lower values
Quantitative Science Studies, 2021 Quantile regression presents a complete picture of the effects on the location, scale, and shape of the dependent variable at all points, not just the mean.
Marzieh Shahmandi +2 more
doaj +1 more source
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 +1 more source
A Bayesian Binary reciprocal LASSO quantile regression (with practical application)
Journal of Kufa for Mathematics and Computer, 2023 Quantile regression is one of the methods that has taken a wide space in application in the previous two decades because of the attractive features of these methods to researchers, as it is not affected by outliers values, meaning that it is considered ...
Mohammed Kahnger, Ahmad Naeem Flaih
doaj +1 more source
Bayesian lasso binary quantile regression
Computational Statistics, 2013 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Benoit, Dries +2 more
openaire +2 more sources
Posterior moments and quantiles for the normal location model with Laplace prior [PDF]
, 2020 We derive explicit expressions for arbitrary moments and quantiles of the posterior distribution of the location parameter eta in the normal location model with Laplace prior, and use the results to approximate the posterior distribution of sums of ...
Franco Peracchi +2 more
core +1 more source
Bayesian analysis of a Tobit quantile regression model [PDF]
, 2007 This paper develops a Bayesian framework for Tobit quantile regression. Our approach is organized around a likelihood function that is based on the asymmetric Laplace dis- tribution, a choice that turns out to be natural in this context.
Stander, J, Yu, K
core +1 more source
Quantile Regression Neural Networks: A Bayesian Approach [PDF]
Journal of Statistical Theory and Practice, 2021 This article introduces a Bayesian neural network estimation method for quantile regression assuming an asymmetric Laplace distribution (ALD) for the response variable. It is shown that the posterior distribution for feedforward neural network quantile regression is asymptotically consistent under a misspecified ALD model. This consistency proof embeds
S. R. Jantre, S. Bhattacharya, T. Maiti
openaire +2 more sources
Bayesian Tobit quantile regression using-prior distribution with ridge parameter [PDF]
, 2014 A Bayesian approach is proposed for coefficient estimation in the Tobit quantile regression model. The proposed approach is based on placing a g-prior distribution depends on the quantile level on the regression coefficients.
Bilias Y +5 more
core +1 more source
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