Empirical Likelihood Confidence Intervals under the Rao-Hartley-Cochran Sampling Design
Empirical Likelihood Confidence Intervals under the Rao-Hartley-Cochran Sampling Design
The Hartley-Rao-Cochran (RHC) sampling design (Rao et al., 1962) is a popular unequal probability sampling design. We show how empirical likelihood confidence intervals can be derived under this sampling design. Berger and De La Riva Torres (2012) proposed an empirical likelihood approach which can be used for point estimation and to construct confidence intervals under complex sampling designs. We show how this approach can be adjusted for the RHC sampling design. The proposed approach intrinsically incorporates sampling weights and auxiliary information. It may give better coverages than standard methods even when the sampling distribution of the parameters of interest is not normal. The proposed approach is simple to implement and less computer intensive than bootstrap. The proposed approach does not rely on re-sampling, linearisation, variance estimation, or design-effects.
auxiliary information, design-based approach, estimating equations, probability proportional to size sampling design, regression estimator, unequal inclusion probabilities
2053-2062
Berger, Yves G.
8fd6af5c-31e6-4130-8b53-90910bf2f43b
Berger, Yves G.
8fd6af5c-31e6-4130-8b53-90910bf2f43b
Berger, Yves G.
(2014)
Empirical Likelihood Confidence Intervals under the Rao-Hartley-Cochran Sampling Design.
JSM 2014 - Joint Statistical Meetings - American Statistical Association, Boston, United States.
02 - 07 Aug 2014.
.
Record type:
Conference or Workshop Item
(Paper)
Abstract
The Hartley-Rao-Cochran (RHC) sampling design (Rao et al., 1962) is a popular unequal probability sampling design. We show how empirical likelihood confidence intervals can be derived under this sampling design. Berger and De La Riva Torres (2012) proposed an empirical likelihood approach which can be used for point estimation and to construct confidence intervals under complex sampling designs. We show how this approach can be adjusted for the RHC sampling design. The proposed approach intrinsically incorporates sampling weights and auxiliary information. It may give better coverages than standard methods even when the sampling distribution of the parameters of interest is not normal. The proposed approach is simple to implement and less computer intensive than bootstrap. The proposed approach does not rely on re-sampling, linearisation, variance estimation, or design-effects.
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e-pub ahead of print date: 2014
Venue - Dates:
JSM 2014 - Joint Statistical Meetings - American Statistical Association, Boston, United States, 2014-08-02 - 2014-08-07
Keywords:
auxiliary information, design-based approach, estimating equations, probability proportional to size sampling design, regression estimator, unequal inclusion probabilities
Organisations:
Statistical Sciences Research Institute
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Local EPrints ID: 377867
URI: http://eprints.soton.ac.uk/id/eprint/377867
PURE UUID: 64325e3c-646c-4e2f-8b5a-9597213766a1
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Date deposited: 24 Jun 2015 11:33
Last modified: 15 Mar 2024 03:01
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