Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/26148
Title: Bayesian scale mixtures of normals linear regression and Bayesian quantile regression with big data and variable selection
Authors: Chu, Y
Yin, Z
Yu, K
Keywords: scale mixtures of normals;quantile regression (QR);Bayesian Inference;big data;normal-inverse-gamma (NIG);variable selection
Issue Date: 9-Mar-2023
Publisher: Elsevier
Citation: Chu, Y., Yin, Z. and Yu, K. (2023) 'Bayesian scale mixtures of normals linear regression and Bayesian quantile regression with big data and variable selection', Journal of Computational and Applied Mathematics, 428, 115192, pp. 1 - 15. doi: 10.1016/j.cam.2023.115192.
Abstract: Quantile regression, which estimates various conditional quantiles of a response variable, including the median (0.5th quantile), is particularly useful when the conditional distribution is asymmetric or heterogeneous or fat-tailed or truncated. Bayesian methods for the inference of quantile regression have been receiving increasing attention from both theoretical and empirical viewpoints but facing the challenge of scaling up when the data are too large to be processed by a single machine under many big data environments nowadays. In this paper, we develop a structure link between Bayesian scale mixtures of normals linear regression and Bayesian quantile regression (BQR) via normal-inverse-gamma (NIG) distribution type of likelihood function, prior distribution and posterior distribution. We further explore the detailed methods of (BQR) for big data, variable selection and posterior prediction. The performance of the proposed techniques is evaluated via simulation studies and a real data analysis.
URI: https://bura.brunel.ac.uk/handle/2438/26148
ISSN: 0377-0427
Other Identifiers: ORCiD: Yuanqi Chu https://orcid.org/0000-0003-2867-9038
115192
ORCiD: Keming Yu https://orcid.org/0000-0001-6341-8402.
Appears in Collections:Dept of Mathematics Research Papers

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