On estimating quantiles using auxiliary information
On estimating quantiles using auxiliary information
We propose a transformation-based approach for estimating quantiles using auxiliary information. The proposed estimators can be easily implemented using a regression estimator. We show that the proposed estimators are consistent and asymptotically unbiased. The main advantage of the proposed estimators is their simplicity. Despite the fact the proposed estimators are not necessarily more efficient than their competitors, they offer a good compromise between accuracy and simplicity. They can be used under single and multistage sampling designs with unequal selection probabilities. A simulation study supports our finding and shows that the proposed estimators are robust and of an acceptable accuracy compared to alternative estimators, which can be more computationally intensive.
distribution function, inclusion probabilities, regression estimator, sample survey
101-119
Berger, Yves G.
8fd6af5c-31e6-4130-8b53-90910bf2f43b
Francisco Munoz, Juan
965f26df-7a02-4db2-98b0-639782663838
1 March 2015
Berger, Yves G.
8fd6af5c-31e6-4130-8b53-90910bf2f43b
Francisco Munoz, Juan
965f26df-7a02-4db2-98b0-639782663838
Berger, Yves G. and Francisco Munoz, Juan
(2015)
On estimating quantiles using auxiliary information.
Journal of Official Statistics, 31 (1), .
(doi:10.1515/JOS-2015-0005).
Abstract
We propose a transformation-based approach for estimating quantiles using auxiliary information. The proposed estimators can be easily implemented using a regression estimator. We show that the proposed estimators are consistent and asymptotically unbiased. The main advantage of the proposed estimators is their simplicity. Despite the fact the proposed estimators are not necessarily more efficient than their competitors, they offer a good compromise between accuracy and simplicity. They can be used under single and multistage sampling designs with unequal selection probabilities. A simulation study supports our finding and shows that the proposed estimators are robust and of an acceptable accuracy compared to alternative estimators, which can be more computationally intensive.
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Berger_Munoz_2015.pdf
- Accepted Manuscript
Available under License Other.
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Accepted/In Press date: 1 November 2014
Published date: 1 March 2015
Keywords:
distribution function, inclusion probabilities, regression estimator, sample survey
Organisations:
Statistical Sciences Research Institute
Identifiers
Local EPrints ID: 355087
URI: http://eprints.soton.ac.uk/id/eprint/355087
ISSN: 0282-423X
PURE UUID: a6593607-f974-440a-8bb8-ca6a030e91e7
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Date deposited: 19 Aug 2013 15:34
Last modified: 15 Mar 2024 03:00
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Author:
Juan Francisco Munoz
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