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New generalized regression estimator in the presence of non response under unequal probability sampling

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  • Nuanpan Lawson
  • Chugiat Ponkaew

Abstract

In this paper, we propose a new generalized regression estimator for the problem of estimating the population total using unequal probability sampling without replacement. A modified automated linearization approach is applied in order to transform the proposed estimator to estimate variance of population total. The variance and estimated value of the variance of the proposed estimator is investigated under a reverse framework assuming that the sampling fraction is negligible and there are equal response probabilities for all units. We prove that the proposed estimator is an asymptotically unbiased estimator and that it does not require a known or estimated response probability to function.

Suggested Citation

  • Nuanpan Lawson & Chugiat Ponkaew, 2019. "New generalized regression estimator in the presence of non response under unequal probability sampling," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 48(10), pages 2483-2498, May.
    • Handle: RePEc:taf:lstaxx:v:48:y:2019:i:10:p:2483-2498
    DOI: 10.1080/03610926.2018.1465091
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