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An Efficient Class of Calibration Ratio Estimators of Domain Mean in Survey Sampling

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Abstract

This paper develops a new approach to domain estimation and proposes a new class of ratio estimators that is more efficient than the regression estimator and not depending on any optimality condition using the principle of calibration weightings. Some well-known regression and ratio-type estimators are obtained and shown to be special members of the new class of estimators. Results of analytical study showed that the new class of estimators is superior in both efficiency and biasedness to all related existing estimators under review. The relative performances of the new class of estimators with a corresponding global estimator were evaluated through a simulation study. Analysis and evaluation are presented.

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Authors and Affiliations

  1. Department of Statistics, University of Calabar, Calabar, Nigeria

    Ekaette I. Enang

  2. Department of Mathematics and Statistics, University of Uyo, Uyo, Nigeria

    Etebong P. Clement

Authors
  1. Ekaette I. Enang
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  2. Etebong P. Clement
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Correspondence to Etebong P. Clement.

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Enang, E.I., Clement, E.P. An Efficient Class of Calibration Ratio Estimators of Domain Mean in Survey Sampling. Commun. Math. Stat. 8, 279–293 (2020). https://doi.org/10.1007/s40304-018-00174-z

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  • DOI: https://doi.org/10.1007/s40304-018-00174-z

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