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DC Field | Value | Language |
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dc.contributor.author | Liu, X | - |
dc.contributor.author | Siverani, S | - |
dc.contributor.author | Yu, K | - |
dc.contributor.author | Smith, KJ | - |
dc.date.accessioned | 2019-10-29T16:16:31Z | - |
dc.date.available | 2019-10-29T16:16:31Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | Liu, X. et al (2019) 'Modelling Tails for Collinear Data with Outliers in the English Longitudinal Study of Ageing: Quantile Profile Regression', Biometrical Journal, 62 (4), pp. 916 - 931. doi: 10.1002/bimj.201900146. | en_US |
dc.identifier.issn | 0323-3847 | - |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/19458 | - |
dc.description.abstract | Research has shown that high blood glucose levels are important predictors of incident diabetes. However, they are also strongly associated with other cardiometabolic risk factors such as high blood pressure, adiposity, and cholesterol, which are also highly correlated with one another. The aim of this analysis was to ascertain how these highly correlated cardiometabolic risk factors might be associated with high levels of blood glucose in older adults aged 50 or older from wave 2 of the English Longitudinal Study of Ageing (ELSA). Due to the high collinearity of predictor variables and our interest in extreme values of blood glucose we proposed a new method, called quantile profile regression, to answer this question. Profile regression, a Bayesian nonparametric model for clustering responses and covariates simultaneously, is a powerful tool to model the relationship between a response variable and covariates, but the standard approach of using a mixture of Gaussian distributions for the response model will not identify the underlying clusters correctly, particularly with outliers in the data or heavy tail distribution of the response. Therefore, we propose quantile profile regression to model the response variable with an asymmetric Laplace distribution, allowing us to model more accurately clusters that are asymmetric and predict more accurately for extreme values of the response variable and/or outliers. Our new method performs more accurately in simulations when compared to Normal profile regression approach as well as robustly when outliers are present in the data. We conclude with an analysis of the ELSA. | - |
dc.description.sponsorship | National Institute for Health Research Method Grant (NIHRRMOFS-2013-03-09) and the National Natural Science Foundation of China (Grant No. 71490725, 11261048, 11371322). | en_US |
dc.format.extent | 916 - 931 | - |
dc.format.medium | Print-Electronic | - |
dc.language.iso | en | en_US |
dc.publisher | Wiley-VCH Verlag | en_US |
dc.rights | Copyright © 2020 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim. All Rights Reserved. This is the peer reviewed version of the following article: Modelling Tails for Collinear Data with Outliers in the English Longitudinal Study of Ageing: Quantile Profile Regression, which has been published in final form at https://doi.org/10.1002/bimj.201900146. This article may be used for non-commercial purposes in accordance with John Wiley & Sons Ltd's Terms and Conditions for Self-Archiving (see: https://authorservices.wiley.com/author-resources/Journal-Authors/licensing/self-archiving.html). | - |
dc.rights.uri | https://authorservices.wiley.com/author-resources/Journal-Authors/licensing/self-archiving.html | - |
dc.subject | asymmetric Laplace distribution | en_US |
dc.subject | Bayesian inference | en_US |
dc.subject | clustering | en_US |
dc.subject | Dirichlet process mixture model | en_US |
dc.subject | profile regression | en_US |
dc.title | Modelling Tails for Collinear Data with Outliers in the English Longitudinal Study of Ageing: Quantile Profile Regression | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.1002/bimj.201900146 | - |
dc.relation.isPartOf | Biometrical Journal | - |
pubs.issue | 4 | - |
pubs.publication-status | Published | - |
pubs.volume | 62 | - |
dc.identifier.eissn | 1521-4036 | - |
Appears in Collections: | Dept of Mathematics Research Papers |
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FullText.pdf | Copyright © 2020 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim. All Rights Reserved. This is the peer reviewed version of the following article: Modelling Tails for Collinear Data with Outliers in the English Longitudinal Study of Ageing: Quantile Profile Regression, which has been published in final form at https://doi.org/10.1002/bimj.201900146. This article may be used for non-commercial purposes in accordance with John Wiley & Sons Ltd's Terms and Conditions for Self-Archiving (see: https://authorservices.wiley.com/author-resources/Journal-Authors/licensing/self-archiving.html). | 534.62 kB | Adobe PDF | View/Open |
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