Design-based predictive inference
Design-based predictive inference
Design-based inference from probability samples is valid by construction for target parameters that are descriptive summaries of finite populations. We develop a novel approach of design-based predictive inference for finite populations, where the individual-level predictor is learned from a proba- bility sample using any models or algorithms for incorporating the relevant auxiliary information, and the uncertainty of estimation is evaluated with respect to the known probability design while the outcome and auxiliary values for modelling are treated as constants. Unlike the existing theory of design-based model-assisted estimation for finite populations, design-based predictive inference is as well suited for individual-level prediction in addition to producing population-level estimates.
Probability sampling, model-assisted estimation, sample split, Rao- Blackwellisation, administrative register, big data
Zhang, Li-Chun
a5d48518-7f71-4ed9-bdcb-6585c2da3649
Sanguiao-Sande, Luis
30d658d0-f699-45d6-9d42-169f415521cb
Lee, Danhyang
f0001fd3-4473-434f-b4d7-1a7046c83875
17 September 2024
Zhang, Li-Chun
a5d48518-7f71-4ed9-bdcb-6585c2da3649
Sanguiao-Sande, Luis
30d658d0-f699-45d6-9d42-169f415521cb
Lee, Danhyang
f0001fd3-4473-434f-b4d7-1a7046c83875
Zhang, Li-Chun, Sanguiao-Sande, Luis and Lee, Danhyang
(2024)
Design-based predictive inference.
Journal of Official Statistics.
Abstract
Design-based inference from probability samples is valid by construction for target parameters that are descriptive summaries of finite populations. We develop a novel approach of design-based predictive inference for finite populations, where the individual-level predictor is learned from a proba- bility sample using any models or algorithms for incorporating the relevant auxiliary information, and the uncertainty of estimation is evaluated with respect to the known probability design while the outcome and auxiliary values for modelling are treated as constants. Unlike the existing theory of design-based model-assisted estimation for finite populations, design-based predictive inference is as well suited for individual-level prediction in addition to producing population-level estimates.
Text
JOS-2023-0162-final
- Accepted Manuscript
More information
Accepted/In Press date: 2 August 2024
Published date: 17 September 2024
Keywords:
Probability sampling, model-assisted estimation, sample split, Rao- Blackwellisation, administrative register, big data
Identifiers
Local EPrints ID: 493760
URI: http://eprints.soton.ac.uk/id/eprint/493760
ISSN: 0282-423X
PURE UUID: 96decee2-77f7-4ff2-975e-8623867b0c04
Catalogue record
Date deposited: 12 Sep 2024 16:38
Last modified: 19 Nov 2024 02:44
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Contributors
Author:
Luis Sanguiao-Sande
Author:
Danhyang Lee
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