Empirical likelihood approaches under complex sampling designs
Empirical likelihood approaches under complex sampling designs
There are two different empirical likelihood approaches for complex sampling designs: “pseudoempirical likelihood” and “unequal probability empirical likelihood”. Both approaches are described and reviewed critically. The key difference is the fact that the self‐normalization property of the pseudoempirical likelihood approach is limited to unidimensional parameters. This property holds for multidimensional parameters, with the unequal probability empirical likelihood approach. This manuscript is a brief description of the key empirical likelihood approaches for complex sampling. This is not an exhaustive account of all the applications of empirical likelihood in survey sampling.
Design-based approach, estimating equations, inclusion probabilities, side information, Stratification
Berger, Yves
8fd6af5c-31e6-4130-8b53-90910bf2f43b
22 March 2018
Berger, Yves
8fd6af5c-31e6-4130-8b53-90910bf2f43b
Berger, Yves
(2018)
Empirical likelihood approaches under complex sampling designs.
In,
Wiley StatsRef: Statistics Reference Online.
Wiley.
(doi:10.1002/9781118445112.stat08066).
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Abstract
There are two different empirical likelihood approaches for complex sampling designs: “pseudoempirical likelihood” and “unequal probability empirical likelihood”. Both approaches are described and reviewed critically. The key difference is the fact that the self‐normalization property of the pseudoempirical likelihood approach is limited to unidimensional parameters. This property holds for multidimensional parameters, with the unequal probability empirical likelihood approach. This manuscript is a brief description of the key empirical likelihood approaches for complex sampling. This is not an exhaustive account of all the applications of empirical likelihood in survey sampling.
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Submitted date: 14 April 2014
Accepted/In Press date: 25 September 2017
e-pub ahead of print date: 22 March 2018
Published date: 22 March 2018
Additional Information:
Associated Publications:
Berger, Y. G., & De La Riva Torres, O. (2016). Empirical likelihood confidence intervals for complex sampling designs. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 78(2), 319-341. DOI: 10.1111/rssb.12115
Oguz Alper, M., & Berger, Y. G. (2016). Modelling complex survey data with population level information: an empirical likelihood approach. Biometrika, 2(103), 447-459. DOI: 10.1093/biomet/asw014
Keywords:
Design-based approach, estimating equations, inclusion probabilities, side information, Stratification
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Local EPrints ID: 414772
URI: http://eprints.soton.ac.uk/id/eprint/414772
PURE UUID: c83bf522-51e7-4c70-8035-9c8371d02161
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Date deposited: 10 Oct 2017 16:31
Last modified: 16 Mar 2024 03:03
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