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Optimal regression estimator for stratified two-stage sampling

Optimal regression estimator for stratified two-stage sampling
Optimal regression estimator for stratified two-stage sampling
Regression estimators are often used in survey sampling for point estimation. We propose a new regression estimator based upon the optimal estimator proposed by Berger et al., (2003). The proposed estimator can be used for stratified two-stage sampling designs when the sampling fraction is negligible and the primary sampling units (PSU) are selected with unequal probabilities. For example, this is the case for self-weighted two-stage designs. We assume that we have auxiliary variables available for the secondary sampling units (SSU) and the primary sampling units (PSU). We propose to use an ultimate cluster approach to estimate the regression coefficient of the regression estimator. Estevao and Särndal (2006) proposed a regression estimator for two-stage sampling. This estimator will be compared with the proposed estimator through a simulation study. We will show that the proposed estimator is more accurate that the Estevao and Särndal (2006) estimator when the strata are homogeneous.
design-based approach, horvitz–thompson estimator, stratification, ultimate cluster approach, unequal inclusion probabilities
978-3-319-05320-2
1431-1968
167-177
Springer
Nangsue, Nuanpan
0bb6ef21-602d-4aa6-80c0-c4da60ca5e7a
Berger, Yves G.
8fd6af5c-31e6-4130-8b53-90910bf2f43b
Mecatti, Fulvia
Conti, Pier Luigi
Ranalli, Maria Giovanna
Nangsue, Nuanpan
0bb6ef21-602d-4aa6-80c0-c4da60ca5e7a
Berger, Yves G.
8fd6af5c-31e6-4130-8b53-90910bf2f43b
Mecatti, Fulvia
Conti, Pier Luigi
Ranalli, Maria Giovanna

Nangsue, Nuanpan and Berger, Yves G. (2014) Optimal regression estimator for stratified two-stage sampling. In, Mecatti, Fulvia, Conti, Pier Luigi and Ranalli, Maria Giovanna (eds.) Contributions to Sampling Statistics. (Contributions to Statistics) Cham, CH. Springer, pp. 167-177. (doi:10.1007/978-3-319-05320-2_11).

Record type: Book Section

Abstract

Regression estimators are often used in survey sampling for point estimation. We propose a new regression estimator based upon the optimal estimator proposed by Berger et al., (2003). The proposed estimator can be used for stratified two-stage sampling designs when the sampling fraction is negligible and the primary sampling units (PSU) are selected with unequal probabilities. For example, this is the case for self-weighted two-stage designs. We assume that we have auxiliary variables available for the secondary sampling units (SSU) and the primary sampling units (PSU). We propose to use an ultimate cluster approach to estimate the regression coefficient of the regression estimator. Estevao and Särndal (2006) proposed a regression estimator for two-stage sampling. This estimator will be compared with the proposed estimator through a simulation study. We will show that the proposed estimator is more accurate that the Estevao and Särndal (2006) estimator when the strata are homogeneous.

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Published date: 9 May 2014
Related URLs:
Keywords: design-based approach, horvitz–thompson estimator, stratification, ultimate cluster approach, unequal inclusion probabilities
Organisations: Statistical Sciences Research Institute

Identifiers

Local EPrints ID: 368587
URI: http://eprints.soton.ac.uk/id/eprint/368587
ISBN: 978-3-319-05320-2
ISSN: 1431-1968
PURE UUID: 43ce1c79-de16-4842-9119-42c3fd790eb5
ORCID for Yves G. Berger: ORCID iD orcid.org/0000-0002-9128-5384

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Date deposited: 10 Sep 2014 11:23
Last modified: 15 Mar 2024 03:01

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Contributors

Author: Nuanpan Nangsue
Author: Yves G. Berger ORCID iD
Editor: Fulvia Mecatti
Editor: Pier Luigi Conti
Editor: Maria Giovanna Ranalli

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