The University of Southampton
University of Southampton Institutional Repository

Nowcasting in triple-system estimation

Nowcasting in triple-system estimation
Nowcasting in triple-system estimation
When samples that each cover part of a population for a certain reference date become available slowly over time, an estimate of the population size can be obtained when at least two samples are available. Ideally one uses all the available samples, but if some samples become available much later one may want to use the samples that are available earlier, to obtain a preliminary or nowcast estimate. However, a limited number of samples may no longer lead to asymptotically unbiased estimates, in particularly in case of two early available samples that suffer from pairwise dependence. In this paper we propose a multiple system nowcasting model that deals with this issue by combining the early available samples with samples from a previous reference date and the expectation-maximisation algorithm. This leads to a nowcast estimate that is asymptotically unbiased under more relaxed assumptions than the dual-system estimator. The multiple system nowcasting model is applied to the problem of estimating the number of homeless people in The Netherlands, which leads to reasonably accurate nowcast estimates.
stat.ME
Zult, Daan B.
ce9fcd6b-0119-443e-af59-d83a69c11cfa
van der Heijden, Peter G.M.
85157917-3b33-4683-81be-713f987fd612
Bakker, Bart F.M.
8b086ec4-999b-4966-bef5-f23cfc098fd7
Zult, Daan B.
ce9fcd6b-0119-443e-af59-d83a69c11cfa
van der Heijden, Peter G.M.
85157917-3b33-4683-81be-713f987fd612
Bakker, Bart F.M.
8b086ec4-999b-4966-bef5-f23cfc098fd7

[Unknown type: UNSPECIFIED]

Record type: UNSPECIFIED

Abstract

When samples that each cover part of a population for a certain reference date become available slowly over time, an estimate of the population size can be obtained when at least two samples are available. Ideally one uses all the available samples, but if some samples become available much later one may want to use the samples that are available earlier, to obtain a preliminary or nowcast estimate. However, a limited number of samples may no longer lead to asymptotically unbiased estimates, in particularly in case of two early available samples that suffer from pairwise dependence. In this paper we propose a multiple system nowcasting model that deals with this issue by combining the early available samples with samples from a previous reference date and the expectation-maximisation algorithm. This leads to a nowcast estimate that is asymptotically unbiased under more relaxed assumptions than the dual-system estimator. The multiple system nowcasting model is applied to the problem of estimating the number of homeless people in The Netherlands, which leads to reasonably accurate nowcast estimates.

Text
2406.17637v1 - Author's Original
Available under License Creative Commons Attribution.
Download (757kB)

More information

Accepted/In Press date: 25 June 2024
Keywords: stat.ME

Identifiers

Local EPrints ID: 491804
URI: http://eprints.soton.ac.uk/id/eprint/491804
PURE UUID: 1204087b-2934-4e09-a2ef-a0be32b22b97
ORCID for Peter G.M. van der Heijden: ORCID iD orcid.org/0000-0002-3345-096X

Catalogue record

Date deposited: 04 Jul 2024 16:43
Last modified: 12 Jul 2024 01:50

Export record

Altmetrics

Contributors

Author: Daan B. Zult
Author: Bart F.M. Bakker

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.

×