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Calibration Approach-Based Regression-Type Estimator for Inverse Relationship Between Study and Auxiliary Variable

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Abstract

A regression-type estimator of population total is developed using the calibration approach under the assumption that the auxiliary variable is negatively correlated with the study variable. An estimator of the variance of the proposed estimator is developed. In addition, a higher order calibration approach has been described for the estimator of the variance of the proposed estimator. When the auxiliary information is not available on all the population units, then a two-phase sampling approach has been suggested. Theoretical results obtained are demonstrated through simulation studies and also with real data. Empirical results show that the proposed estimator outperforms the usual regression estimator in terms of the criteria of bias and mean square error.

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Authors and Affiliations

  1. Division of Sample Survey, Indian Agricultural Statistics Research Institute, Library Avenue, PUSA Campus, New Delhi, 110012, India

    U. C. Sud, Hukum Chandra & V. K. Gupta

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  1. U. C. Sud
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  2. Hukum Chandra
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  3. V. K. Gupta
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Correspondence to Hukum Chandra.

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Sud, U.C., Chandra, H. & Gupta, V.K. Calibration Approach-Based Regression-Type Estimator for Inverse Relationship Between Study and Auxiliary Variable. J Stat Theory Pract 8, 707–721 (2014). https://doi.org/10.1080/15598608.2013.832643

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  • DOI: https://doi.org/10.1080/15598608.2013.832643

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