On estimating quantiles using auxiliary information
On estimating quantiles using auxiliary information
Estimation of quantiles may be of considerable interest when measuring income distribution and poverty lines. For instance, the median is regarded as a more appropriate measure of location than the mean when variables, such as income, expenditure, etc, exhibit highly skewed distributions. In sample surveys, auxiliary information is often used at the estimation stage to increase the precision of estimators of means. The use of auxiliary information has been studied extensively for estimation of means, but it has no obvious extensions to the estimation of quantiles. A novel method for estimating quantiles using auxiliary information is proposed. The proposed estimator is based upon the regression estimator of a transformed variable of interest. Simulation studies support our findings and show that the proposed estimator can be more accurate than or as accurate as alternative estimators (Chambers & Dunstan, 1986; Rao et al. 1990; Silva & Skinner 1995) which can be computationally more intensive.
distribution function, Horvitz-Thompson estimator, regression estimator, sample surveys
3313-3318
Berger, Yves G.
8fd6af5c-31e6-4130-8b53-90910bf2f43b
Juan, Francisco Muñoz
b14c3fcd-adce-4043-be29-c8e07ae8039d
21 August 2011
Berger, Yves G.
8fd6af5c-31e6-4130-8b53-90910bf2f43b
Juan, Francisco Muñoz
b14c3fcd-adce-4043-be29-c8e07ae8039d
Berger, Yves G. and Juan, Francisco Muñoz
(2011)
On estimating quantiles using auxiliary information.
58th World Statistical Congress (International Statistical Institute).
21 - 26 Aug 2011.
.
Record type:
Conference or Workshop Item
(Paper)
Abstract
Estimation of quantiles may be of considerable interest when measuring income distribution and poverty lines. For instance, the median is regarded as a more appropriate measure of location than the mean when variables, such as income, expenditure, etc, exhibit highly skewed distributions. In sample surveys, auxiliary information is often used at the estimation stage to increase the precision of estimators of means. The use of auxiliary information has been studied extensively for estimation of means, but it has no obvious extensions to the estimation of quantiles. A novel method for estimating quantiles using auxiliary information is proposed. The proposed estimator is based upon the regression estimator of a transformed variable of interest. Simulation studies support our findings and show that the proposed estimator can be more accurate than or as accurate as alternative estimators (Chambers & Dunstan, 1986; Rao et al. 1990; Silva & Skinner 1995) which can be computationally more intensive.
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Published date: 21 August 2011
Venue - Dates:
58th World Statistical Congress (International Statistical Institute), 2011-08-21 - 2011-08-26
Keywords:
distribution function, Horvitz-Thompson estimator, regression estimator, sample surveys
Organisations:
Statistical Sciences Research Institute
Identifiers
Local EPrints ID: 354877
URI: http://eprints.soton.ac.uk/id/eprint/354877
PURE UUID: 4fbc8206-a102-4a4d-93ee-c2ecf13796f3
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Date deposited: 07 Aug 2013 10:08
Last modified: 15 Mar 2024 03:00
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Author:
Francisco Muñoz Juan
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