Aykroyd, RG orcid.org/0000-0003-3700-0816 (2019) Bayesian modeling of temperature-related mortality with latent functional relationships. Communications in Statistics - Theory and Methods, 48 (1). pp. 3-14. ISSN 0361-0926
Abstract
It is common for the mortality rate to increase during periods of extreme temperature and for the minimum mortality rate to depend on factors such as the mean summer temperature. In this paper, local correlation is explicitly described using a generalized additive model with a spatial component which allows information from neighbouring locations to be combined. Random walk and random field models are proposed to describe temporal and spatial correlation structure. Further, joint spatial-temporal modeling is proposed by including a temperature-related mortality term. This will make use of existing data more efficiently and should reduce prediction variability. The methods are illustrated using simulated data based on real mortality and temperature data.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2017, Taylor & Francis Group, LLC. This is an Accepted Manuscript of an article published by Taylor & Francis in Communications in Statistics - Theory and Methods on 16 Jan 2018, available online: https://doi.org/10.1080/03610926.2017.1421223 |
Keywords: | Bayesian methods, Demography, Generalised additive models, Maximum likelihood, Spatial-temporal. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mathematics (Leeds) > Statistics (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 19 Dec 2017 11:55 |
Last Modified: | 21 May 2019 13:42 |
Status: | Published |
Publisher: | Taylor & Francis |
Identification Number: | 10.1080/03610926.2017.1421223 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:125321 |