Results 11 to 20 of about 10,621 (115)

A Bayesian Variable Selection Method for Spatial Autoregressive Quantile Models

open access: yesMathematics, 2023
In this paper, a Bayesian variable selection method for spatial autoregressive (SAR) quantile models is proposed on the basis of spike and slab prior for regression parameters.
Yuanying Zhao, Dengke Xu
doaj   +1 more source

Bayesian composite quantile regression for the single-index model.

open access: yesPLoS 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

Quantity and Quality in Scientific Productivity: The Tilted Funnel Goes Bayesian

open access: yesJournal of Intelligence, 2022
The equal odds baseline model of creative scientific productivity proposes that the number of high-quality works depends linearly on the number of total works.
Boris Forthmann, Denis Dumas
doaj   +1 more source

Simulation Study The Using of Bayesian Quantile Regression in Nonnormal Error

open access: yesCauchy: Jurnal Matematika Murni dan Aplikasi, 2018
The purposes of this paper is  to introduce the ability of the Bayesian quantile regression method in overcoming the problem of the nonnormal errors using asymmetric laplace distribution on simulation study.
Catrin Muharisa   +2 more
doaj   +1 more source

A Bayesian hurdle quantile regression model for citation analysis with mass points at lower values

open access: yesQuantitative 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

A Bayesian Binary reciprocal LASSO quantile regression (with practical application)

open access: yesJournal 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 variable selection in linear quantile mixed models for longitudinal data with application to macular degeneration.

open access: yesPLoS 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

Power Prior Elicitation in Bayesian Quantile Regression

open access: yesJournal of Probability and Statistics, 2011
We address a quantile dependent prior for Bayesian quantile regression. We extend the idea of the power prior distribution in Bayesian quantile regression by employing the likelihood function that is based on a location-scale mixture representation of ...
Rahim Alhamzawi, Keming Yu
doaj   +1 more source

bayesQR: A Bayesian Approach to Quantile Regression

open access: yesJournal 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

Anomaly Detection in Health Insurance Claims Using Bayesian Quantile Regression

open access: yesInternational Journal of Mathematics and Mathematical Sciences, 2021
Research has shown that current health expenditure in most countries, especially in sub-Saharan Africa, is inadequate and unsustainable. Yet, fraud, abuse, and waste in health insurance claims by service providers and subscribers threaten the delivery of
Ezekiel N. N. Nortey   +4 more
doaj   +1 more source

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