Results 11 to 20 of about 812,319 (279)

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
core   +7 more sources

Simulation Study of Autocorrelated Error Using Bayesian Quantile Regression

Science and Technology Indonesia, 2020
  The purpose of this study is to compare the ability of the Classical Quantile Regression method and the Bayesian Quantile Regression method in estimating models that contain autocorrelated error problems using simulation studies.
Nayla Desviona, Ferra Yanuar
doaj   +1 more source

Posterior moments and quantiles for the normal location model with Laplace prior [PDF]

Communications in Statistics - Theory and Methods, 2020
We derive explicit expressions for arbitrary moments and quantiles of the posterior distribution of the location parameter η in the normal location model with Laplace prior, and use the results to approximate the posterior distribution of sums of independent copies of η.
Giuseppe De Luca, Jan R. Magnus, Franco Peracchi   +2 more
openaire   +4 more sources

Posterior Inference in Bayesian Quantile Regression with Asymmetric Laplace Likelihood [PDF]

International Statistical Review, 2015
Summary The paper discusses the asymptotic validity of posterior inference of pseudo‐Bayesian quantile regression methods with complete or censored data when an asymmetric Laplace likelihood is used. The asymmetric Laplace likelihood has a special place in the Bayesian quantile regression framework because the usual quantile ...
Yang, Yunwen, Wang, Huixia Judy, He, Xuming   +2 more
openaire   +3 more sources

Posterior Inference for Quantile Regression: Adaptation to Sparsity

, 2021
Quantile regression is a powerful data analysis tool that accommodates heterogeneous covariate-response relationships. We find that by coupling the asymmetric Laplace working likelihood with appropriate shrinkage priors, we can deliver posterior inference that automatically adapts to possible sparsity in quantile regression analysis.
Li, Yuanzhi, He, Xuming
openaire   +2 more sources

Bayesian analysis for quantile smoothing spline

Statistical Theory and Related Fields, 2021
In Bayesian quantile smoothing spline [Thompson, P., Cai, Y., Moyeed, R., Reeve, D., & Stander, J. (2010). Bayesian nonparametric quantile regression using splines.
Zhongheng Cai, Dongchu Sun
doaj   +1 more source

Quantile pyramids for Bayesian nonparametrics [PDF]

, 2009
Polya trees fix partitions and use random probabilities in order to construct random probability measures. With quantile pyramids we instead fix probabilities and use random partitions.
Hjort, Nils Lid, Walker, Stephen G.
core   +1 more source

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

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   +1 more source

Differentially private posterior summaries for linear regression coefficients

The Journal of Privacy and Confidentiality, 2018
In Bayesian regression modeling, often analysts summarize inferences using posterior probabilities and quantiles, such as the posterior probability that a coefficient exceeds zero or the posterior median of that coefficient.
Gilad Amitai, Jerome Reiter
doaj   +1 more source