Results 21 to 30 of about 113,685 (321)
Modeling Spatial Data with Heteroscedasticity Using PLVCSAR Model: A Bayesian Quantile Regression Approach [PDF]
EntropySpatial data not only enables smart cities to visualize, analyze, and interpret data related to location and space, but also helps departments make more informed decisions.
Rongshang Chen, Zhiyong Chen
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Bayesian Lasso-mixed quantile regression [PDF]
Journal of Statistical Computation and Simulation, 2012 In this paper, we discuss the regularization in linear-mixed quantile regression. A hierarchical Bayesian model is used to shrink the fixed and random effects towards the common population values by introducing an l1 penalty in the mixed quantile regression check function.
Alhamzawi, R, Yu, K
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Simulation Study The Using of Bayesian Quantile Regression in Nonnormal Error
Cauchy: 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
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Bayesian quantile regression for single-index models [PDF]
Statistics and Computing, 2012 Using an asymmetric Laplace distribution, which provides a mechanism for Bayesian inference of quantile regression models, we develop a fully Bayesian approach to fitting single-index models in conditional quantile regression. In this work, we use a Gaussian process prior for the unknown nonparametric link function and a Laplace distribution on the ...
Hu, Yuao, Lian, Heng, Gramacy, Robert B.
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Bayesian semiparametric additive quantile regression [PDF]
Statistical Modelling, 2013 Quantile regression provides a convenient framework for analyzing the impact of covariates on the complete conditional distribution of a response variable instead of only the mean. While frequentist treatments of quantile regression are typically completely nonparametric, a Bayesian formulation relies on assuming the asymmetric Laplace distribution as
Yue, Yu Ryan +4 more
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qgam: Bayesian Nonparametric Quantile Regression Modeling in R
Journal of Statistical Software, 2021 Generalized additive models (GAMs) are flexible non-linear regression models, which can be fitted efficiently using the approximate Bayesian methods provided by the mgcv R package.
Matteo Fasiolo +4 more
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bqror: An R package for Bayesian Quantile Regression in Ordinal Models [PDF]
The R Journal, 2021 This article describes an R package bqror that estimates Bayesian quantile regression for ordinal models introduced in Rahman (2016). The paper classifies ordinal models into two types and offers computationally efficient, yet simple, Markov chain Monte ...
P. Maheshwari, M. A. Rahman
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A Bayesian Variable Selection Method for Spatial Autoregressive Quantile Models
Mathematics, 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
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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 +3 more
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Quantity and Quality in Scientific Productivity: The Tilted Funnel Goes Bayesian
Journal 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
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