The University of Southampton
University of Southampton Institutional Repository

Bayesian functional models for population forecasting

Bayesian functional models for population forecasting
Bayesian functional models for population forecasting
Shang, Han Lin
1625ecc8-1ab1-4ef8-b87e-e5a2382d34c5
Wisniowski, Arkadiusz
ec9da054-45f0-4393-ad91-87e8f9750ae9
Bijak, Jakub
e33bf9d3-fca6-405f-844c-4b2decf93c66
Smith, Peter W.F.
961a01a3-bf4c-43ca-9599-5be4fd5d3940
Raymer, James
ed2973c1-b78d-4166-baf3-4e18f1b24070
Shang, Han Lin
1625ecc8-1ab1-4ef8-b87e-e5a2382d34c5
Wisniowski, Arkadiusz
ec9da054-45f0-4393-ad91-87e8f9750ae9
Bijak, Jakub
e33bf9d3-fca6-405f-844c-4b2decf93c66
Smith, Peter W.F.
961a01a3-bf4c-43ca-9599-5be4fd5d3940
Raymer, James
ed2973c1-b78d-4166-baf3-4e18f1b24070

Shang, Han Lin, Wisniowski, Arkadiusz, Bijak, Jakub, Smith, Peter W.F. and Raymer, James (2013) Bayesian functional models for population forecasting. Joint Eurostat/UNECE Work Session on Demographic Projections, Rome, Italy. 29 - 31 Oct 2013.

Record type: Conference or Workshop Item (Other)
Text
Shang_et_al_Bayesian functional models.pdf - Author's Original
Download (619kB)

More information

Published date: 30 October 2013
Venue - Dates: Joint Eurostat/UNECE Work Session on Demographic Projections, Rome, Italy, 2013-10-29 - 2013-10-31
Organisations: Social Statistics & Demography

Identifiers

Local EPrints ID: 359802
URI: http://eprints.soton.ac.uk/id/eprint/359802
PURE UUID: 4a3963b7-a177-4660-aa2e-f30df459707b
ORCID for Jakub Bijak: ORCID iD orcid.org/0000-0002-2563-5040
ORCID for Peter W.F. Smith: ORCID iD orcid.org/0000-0003-4423-5410

Catalogue record

Date deposited: 13 Nov 2013 13:42
Last modified: 15 Mar 2024 03:34

Export record

Contributors

Author: Han Lin Shang
Author: Arkadiusz Wisniowski
Author: Jakub Bijak ORCID iD
Author: James Raymer

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×