<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></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.

On Estimating Quantiles Using Auxiliary Information

Southampton Statistical Sciences Research Institute, University of Southampton, Southampton, SO17 1BJ, UK

Department of Quantitative Methods in Economics and Business, University of Granada, Granada, 18071, Spain

Journal of Official Statistics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"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...

On Estimating Quantiles Using Auxiliary Information

Published Online: Mar 01, 2015

Page range: 101 - 119

Received: Oct 01, 2013

Accepted: Nov 01, 2014

DOI: https://doi.org/10.1515/jos-2015-0005

Keywords
Distribution function, inclusion probabilities, regression estimator, sample survey

© 2015 Yves G. Berger and Juan F. Munoz

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

On Estimating Quantiles Using Auxiliary Information

Published Online: Mar 01, 2015

Page range: 101 - 119

Received: Oct 01, 2013

Accepted: Nov 01, 2014

DOI: https://doi.org/10.1515/jos-2015-0005

KeywordsDistribution function, inclusion probabilities, regression estimator, sample survey

© 2015 Yves G. Berger and Juan F. Munoz

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Keywords
Distribution function, inclusion probabilities, regression estimator, sample survey