Empirical likelihood inference for the Rao-Hartley-Cochran
sampling design
Empirical likelihood inference for the Rao-Hartley-Cochran
sampling design
The Hartley-Rao-Cochran sampling design is an unequal probability sampling design which can be used to select samples from finite populations. We propose to adjust the empirical likelihood approach for the Hartley-Rao-Cochran sampling design. The approach proposed intrinsically incorporates sampling weights, auxiliary information and allows for large sampling fractions. It can be used to construct confidence intervals. In a simulation study, we show that the coverage may be better for the empirical likelihood confidence interval than for standard confidence intervals based on variance estimates. The approach proposed is simple to implement and less computer intensive than bootstrap. The confidence interval proposed does not rely on re-sampling, linearization, variance estimation, design-effects or joint inclusion probabilities.
721-735
Berger, Yves G.
8fd6af5c-31e6-4130-8b53-90910bf2f43b
September 2016
Berger, Yves G.
8fd6af5c-31e6-4130-8b53-90910bf2f43b
Berger, Yves G.
(2016)
Empirical likelihood inference for the Rao-Hartley-Cochran
sampling design.
Scandinavian Journal of Statistics, 43 (3), .
(doi:10.1111/sjos.12200).
Abstract
The Hartley-Rao-Cochran sampling design is an unequal probability sampling design which can be used to select samples from finite populations. We propose to adjust the empirical likelihood approach for the Hartley-Rao-Cochran sampling design. The approach proposed intrinsically incorporates sampling weights, auxiliary information and allows for large sampling fractions. It can be used to construct confidence intervals. In a simulation study, we show that the coverage may be better for the empirical likelihood confidence interval than for standard confidence intervals based on variance estimates. The approach proposed is simple to implement and less computer intensive than bootstrap. The confidence interval proposed does not rely on re-sampling, linearization, variance estimation, design-effects or joint inclusion probabilities.
Text
Berger_2016_Pre.pdf
- Accepted Manuscript
More information
Accepted/In Press date: 22 September 2015
e-pub ahead of print date: 22 December 2015
Published date: September 2016
Organisations:
Statistical Sciences Research Institute
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Local EPrints ID: 374130
URI: http://eprints.soton.ac.uk/id/eprint/374130
ISSN: 0303-6898
PURE UUID: 464e1888-28cc-4256-b5eb-1e2680d46453
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Date deposited: 06 Feb 2015 11:00
Last modified: 15 Mar 2024 03:01
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