Empirical likelihood multiplicity adjusted estimator for multiple frame surveys
Empirical likelihood multiplicity adjusted estimator for multiple frame surveys
Multiple frame surveys are commonly used for a variety of reasons, including correcting for frame undercoverage, increasing the precision of estimators of population parameters for groups of interest, targeting rare populations and reducing survey costs. Several approximately design unbiased estimators have been proposed for inference from multiple frame surveys. Singh & Mecatti (2011) generalized most of the existing estimators as a class of Generalized Multiplicity-Adjusted Horvitz-Thompson Estimators. We develop an Empirical Likelihood approach to the Multiplicity-adjusted estimator. The proposed estimator allows for several multiplicity adjustments. It can handle auxiliary information and can be applied to a variety of parameters of interest expressed as unique solutions to estimating equations. Under certain sampling designs, Wilks-type confidence intervals can be calculated without variance estimates.
Kabzinska, Ewa
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Berger, Yves
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Pratesi, Monica
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Pena, Cira
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Kabzinska, Ewa
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Berger, Yves
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Pratesi, Monica
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Pena, Cira
1ed6ea26-7c3f-4967-aaa9-8750e8bb9820
Kabzinska, Ewa and Berger, Yves
(2016)
Empirical likelihood multiplicity adjusted estimator for multiple frame surveys.
Pratesi, Monica and Pena, Cira
(eds.)
SIS2016: 48th Scientific Meeting of the Italian Statistical Society, Salerno, Italy.
08 - 10 Jun 2016.
6 pp
.
Record type:
Conference or Workshop Item
(Paper)
Abstract
Multiple frame surveys are commonly used for a variety of reasons, including correcting for frame undercoverage, increasing the precision of estimators of population parameters for groups of interest, targeting rare populations and reducing survey costs. Several approximately design unbiased estimators have been proposed for inference from multiple frame surveys. Singh & Mecatti (2011) generalized most of the existing estimators as a class of Generalized Multiplicity-Adjusted Horvitz-Thompson Estimators. We develop an Empirical Likelihood approach to the Multiplicity-adjusted estimator. The proposed estimator allows for several multiplicity adjustments. It can handle auxiliary information and can be applied to a variety of parameters of interest expressed as unique solutions to estimating equations. Under certain sampling designs, Wilks-type confidence intervals can be calculated without variance estimates.
Text
Kabzinska_Berger_ISS_2016.pdf
- Author's Original
More information
e-pub ahead of print date: June 2016
Venue - Dates:
SIS2016: 48th Scientific Meeting of the Italian Statistical Society, Salerno, Italy, 2016-06-08 - 2016-06-10
Organisations:
Statistical Sciences Research Institute
Identifiers
Local EPrints ID: 397752
URI: http://eprints.soton.ac.uk/id/eprint/397752
PURE UUID: bef9cb3e-3a85-4ba4-8c1d-ae0f104ad9ea
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Date deposited: 06 Jul 2016 08:59
Last modified: 15 Mar 2024 03:01
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Contributors
Author:
Ewa Kabzinska
Editor:
Monica Pratesi
Editor:
Cira Pena
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