Skip to content
Publish & Distribute
Publishing Solutions
Distribution Solutions
Library Services
Subjects
Architecture and Design
Arts
Business and Economics
Chemistry
Classical and Ancient Near Eastern Studies
Computer Sciences
Cultural Studies
Engineering
General Interest
Geosciences
History
Industrial Chemistry
Jewish Studies
Law
Library and Information Science, Book Studies
Life Sciences
Linguistics and Semiotics
Literary Studies
Materials Sciences
Mathematics
Medicine
Music
Pharmacy
Philosophy
Physics
Social Sciences
Sports and Recreation
Theology and Religion
Publications
Journals
Books
Proceedings
Publishers
Journal Matcher
Blog
Contact
Search
English
English
Deutsch
Polski
Español
Français
Italiano
Cart
Home
Journals
Journal of Official Statistics
Volume 37 (2021): Issue 3 (September 2021)
Open Access
Modelling Frontier Mortality Using Bayesian Generalised Additive Models
Jason Hilton
Jason Hilton
Department of Social Statistics and Demography, University of Southampton
Search for this author on
Sciendo
|
Google Scholar
Hilton, Jason
,
Erengul Dodd
Erengul Dodd
School of Mathematical Sciences, University of Southampton
Search for this author on
Sciendo
|
Google Scholar
Dodd, Erengul
,
Jonathan J. Forster
Jonathan J. Forster
Department of Statistics, University of Warwick
Search for this author on
Sciendo
|
Google Scholar
Forster, Jonathan J.
and
Peter W.F. Smith
Peter W.F. Smith
Department of Social Statistics and Demography, University of Southampton
Search for this author on
Sciendo
|
Google Scholar
Smith, Peter W.F.
Sep 13, 2021
Journal of Official Statistics
Volume 37 (2021): Issue 3 (September 2021)
About this article
Previous Article
Next Article
Abstract
References
Authors
Articles in this Issue
Preview
PDF
Cite
Share
Download Cover
Published Online:
Sep 13, 2021
Page range:
569 - 589
Received:
Nov 01, 2019
Accepted:
Nov 01, 2020
DOI:
https://doi.org/10.2478/jos-2021-0026
Keywords
Mortality
,
demography
,
Bayesian methods
,
population forecasting
© 2021 Jason Hilton et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.