Results 1 to 10 of about 13,060 (137)

Gibbs Posterior Inference on Multivariate Quantiles [PDF]

Journal of Statistical Planning and Inference, 2022
Bayesian and other likelihood-based methods require specification of a statistical model and may not be fully satisfactory for inference on quantities, such as quantiles, that are not naturally defined as model parameters. In this paper, we construct a direct and model-free Gibbs posterior distribution for multivariate quantiles. Being model-free means
Ryan Martin, Indrabati Bhattacharya
exaly   +4 more sources

Validation of Software for Bayesian Models Using Posterior Quantiles [PDF]

Journal of Computational and Graphical Statistics, 2006
This article presents a simulation-based method designed to establish the computational correctness of software developed to fit a specific Bayesian model, capitalizing on properties of Bayesian posterior distributions. We illustrate the validation technique with two examples.
Andrew Gelman, Donald B Rubin
exaly   +2 more sources

Bayesian Quantile Estimation and Regression With Martingale Posteriors [PDF]

Journal of the Royal Statistical Society Series B: Statistical Methodology
Abstract Quantile estimation and regression within the Bayesian framework is challenging as the choice of likelihood and prior is not obvious. In this paper, we introduce a novel Bayesian nonparametric method for quantile estimation and regression based on the recently introduced martingale posterior (MP) framework.
Fong, Edwin, Yiu, Andrew
exaly   +3 more sources

Combined Effects of Metals and PFAS Exposure on Prevalent Diabetes [PDF]

Journal of Xenobiotics
Human per- and polyfluoroalkyl substances (PFAS) and metal exposures occur as mixtures, but most studies evaluate them independently. Using data from adults in the 2017–2018 National Health and Nutrition Examination Survey (NHANES) with directly measured
Rifa Tasnia, Emmanuel Obeng-Gyasi
doaj   +2 more sources

Bayesian Estimation of Marginal Quantiles with Missing Data in a Multivariate Regression Framework [PDF]

Entropy
In this article, we propose and study a class of multivariate regression models that account for ignorable missing data in skewed, potentially heavy-tailed response vectors with positive components.
Raúl Alejandro Morán-Vásquez, Mauricio A. Mazo-Lopera, Jose Antonio Escobar-Arias   +2 more
doaj   +2 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
Dirk van den Poel
exaly   +3 more sources

Posterior Consistency of Bayesian Quantile Regression Based on the Misspecified Asymmetric Laplace Density

Bayesian Analysis, 2013
We explore an asymptotic justification for the widely used and empirically verified approach of assuming an asymmetric Laplace distribution (ALD) for the response in Bayesian Quantile Regression. Based on empirical findings, Yu and Moyeed (2001) argued that the use of ALD is satisfactory even if it is not the true underlying distribution.
Pulak Ghosh, Karthik Sriram
exaly   +4 more sources

Posterior Rates of Convergence for Composite Quantile Regression

Communications in Statistics - Theory and Methods
Composite quantile regression is based on the convex combination of single quantile quantile loss functions and enjoys many advantages over single quantile regression. The Bayesian extension is based on the finite mixture of asymmetric Laplace densities.
Shaobo Jin, Lukas Arnroth
exaly   +3 more sources

Modeling Length of Hospital Stay for Patients With COVID-19 in West Sumatra Using Quantile Regression Approach

Cauchy: Jurnal Matematika Murni dan Aplikasi, 2021
This study aims to construct the model for the length of hospital stay for patients with COVID-19 using quantile regression and Bayesian quantile approaches.
Ferra Yanuar, Athifa Salsabila Deva, Maiyastri Maiyastri, Hazmira Yozza, Aidinil Zetra   +4 more
doaj   +1 more source

Bayesian quantile semiparametric mixed-effects double regression models

Statistical Theory and Related Fields, 2021
Semiparametric mixed-effects double regression models have been used for analysis of longitudinal data in a variety of applications, as they allow researchers to jointly model the mean and variance of the mixed-effects as a function of predictors ...
Duo Zhang, Liucang Wu, Keying Ye, Min Wang   +3 more
doaj   +1 more source