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Multivariate structure preserving estimation for population compositions

Multivariate structure preserving estimation for population compositions
Multivariate structure preserving estimation for population compositions
This document introduces a new Structure Preserving Estimator for Small Area compositions, using data from a proxy and a sample compositions. The proposed estimator, the Multivariate Structure Preserving Estimator (MSPREE), extends the two main SPREE-type estimators: the SPREE and the GSPREE. The additional flexibility of the MSPREE may lead to estimates with less MSE than its predecessors. An extension of the MSPREE including cell specific random effects, the Mixed MSPREE (MMSPREE), is also presented, in an attempt to further reduce the size of the bias when the associated sample size allows for it. In order to estimate the variance components governing the variance structure of the random effects in the MMSPREE, an unbiased moment-type estimator is proposed. Furthermore, an estimator for the variance of the MSPREE, as well as methodologies to evaluate the unconditional and finite population MSE of both MSPREE and MMSPREE, are developed. The behaviour of the proposed estimators is illustrated in a controlled setting via a simulation exercise, and in a real data application.
Luna Hernandez, Angela
18c32be6-a60c-4d33-aae1-76ab69ca6d50
Luna Hernandez, Angela
18c32be6-a60c-4d33-aae1-76ab69ca6d50
Tzavidis, Nikolaos
431ec55d-c147-466d-9c65-0f377b0c1f6a
Zhang, Li
a5d48518-7f71-4ed9-bdcb-6585c2da3649

(2016) Multivariate structure preserving estimation for population compositions. University of Southampton, School of Social Sciences, Doctoral Thesis, 155pp.

Record type: Thesis (Doctoral)

Abstract

This document introduces a new Structure Preserving Estimator for Small Area compositions, using data from a proxy and a sample compositions. The proposed estimator, the Multivariate Structure Preserving Estimator (MSPREE), extends the two main SPREE-type estimators: the SPREE and the GSPREE. The additional flexibility of the MSPREE may lead to estimates with less MSE than its predecessors. An extension of the MSPREE including cell specific random effects, the Mixed MSPREE (MMSPREE), is also presented, in an attempt to further reduce the size of the bias when the associated sample size allows for it. In order to estimate the variance components governing the variance structure of the random effects in the MMSPREE, an unbiased moment-type estimator is proposed. Furthermore, an estimator for the variance of the MSPREE, as well as methodologies to evaluate the unconditional and finite population MSE of both MSPREE and MMSPREE, are developed. The behaviour of the proposed estimators is illustrated in a controlled setting via a simulation exercise, and in a real data application.

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More information

Published date: October 2016
Organisations: University of Southampton, Social Statistics & Demography

Identifiers

Local EPrints ID: 404689
URI: http://eprints.soton.ac.uk/id/eprint/404689
PURE UUID: c2676fff-b132-483b-a34a-b2ffdd158e8e
ORCID for Nikolaos Tzavidis: ORCID iD orcid.org/0000-0002-8413-8095
ORCID for Li Zhang: ORCID iD orcid.org/0000-0002-3944-9484

Catalogue record

Date deposited: 30 Jan 2017 16:04
Last modified: 27 Jul 2019 00:31

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Contributors

Author: Angela Luna Hernandez
Thesis advisor: Nikolaos Tzavidis ORCID iD
Thesis advisor: Li Zhang ORCID iD

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