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DC Field | Value | Language |
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dc.contributor.author | Chu, Y | - |
dc.contributor.author | Yin, Z | - |
dc.contributor.author | Yu, K | - |
dc.date.accessioned | 2023-03-14T19:27:46Z | - |
dc.date.available | 2023-03-14T19:27:46Z | - |
dc.date.issued | 2023-03-09 | - |
dc.identifier | ORCiD: Yuanqi Chu https://orcid.org/0000-0003-2867-9038 | - |
dc.identifier | 115192 | - |
dc.identifier | ORCiD: Keming Yu https://orcid.org/0000-0001-6341-8402. | - |
dc.identifier.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. | en_US |
dc.identifier.issn | 0377-0427 | - |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/26148 | - |
dc.description.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. | en_US |
dc.description.sponsorship | National Social Science Foundation of China (Series number: 21BTJ040); Office for National Statistics (ONS) ref: PU-19-0235 (A new quantile regression with application to the analysis of bounded economic variables). | en_US |
dc.format.extent | 1 - 15 | - |
dc.format.medium | Print-Electronic | - |
dc.language.iso | en_US | en_US |
dc.publisher | Elsevier | en_US |
dc.rights | Copyright © 2023 Elsevier. All rights reserved. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1016/j.cam.2023.115192, made available on this repository under a Creative Commons CC BY-NC-ND attribution licence (https://creativecommons.org/licenses/by-nc-nd/4.0/). | - |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0/ | - |
dc.subject | scale mixtures of normals | en_US |
dc.subject | quantile regression (QR) | en_US |
dc.subject | Bayesian Inference | en_US |
dc.subject | big data | en_US |
dc.subject | normal-inverse-gamma (NIG) | en_US |
dc.subject | variable selection | en_US |
dc.title | Bayesian scale mixtures of normals linear regression and Bayesian quantile regression with big data and variable selection | en_US |
dc.type | Article | en_US |
dc.relation.isPartOf | Journal of Computational and Applied Mathematics | - |
pubs.publication-status | Published | - |
pubs.volume | 428 | - |
dc.identifier.eissn | 1879-1778 | - |
dc.description.version | Data availability: The data is publicly available. | - |
dc.rights.holder | Elsevier | - |
Appears in Collections: | Dept of Mathematics Research Papers |
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FullText.pdf | Copyright © 2023 Elsevier. All rights reserved. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1016/j.cam.2023.115192, made available on this repository under a Creative Commons CC BY-NC-ND attribution licence (https://creativecommons.org/licenses/by-nc-nd/4.0/). | 507.28 kB | Adobe PDF | View/Open |
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