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Model based estimation of population total in presence of non-ignorable non-response

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  • Shakeel Ahmed
  • Javid Shabbir

Abstract

The problem of handling non-ignorable non-response has been typically addressed under the design-based approach using the well-known sub-sampling technique introduced by Hansen and Hurwitz [1946, Journal of the American Statistical Association, Vol 41(236), Page 517- 529]. Alternatively, the model-based paradigm emphasizes on utilizing the underlying model relationship between the outcome variable and one or more covariate(s) whose population values are known prior to the survey. This article utilizes the model relationship between the study variable and covariate(s) for handling non-ignorable non-response and obtaining an unbiased estimator for the population total under the sub-sampling technique. The main idea is to combine the estimates obtained from the sample on first call and the sub-sample from second call using separate model relationships. The contribution of this paper helps us in providing unbiased estimates with an improved efficiency under model-based paradigm in presence of non-ignorable non-response. The provided method is more economical than the available estimators under callback methods as we are working sub-sampling and also increase response rate as a stronger mode of interview is employed for data collection. A numerical study using Monte Carlo is presented to illustrate the behavior of the proposed and the efficiency comparison.

Suggested Citation

  • Shakeel Ahmed & Javid Shabbir, 2019. "Model based estimation of population total in presence of non-ignorable non-response," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-19, October.
    • Handle: RePEc:plo:pone00:0222701
    DOI: 10.1371/journal.pone.0222701
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    References listed on IDEAS

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    1. Barton, J. & Bain, C. & Hennekens, C.H. & Rosner, B. & Belanger, C. & Roth, A. & Speizer, F.E., 1980. "Characteristics of respondents and non-respondents to a mailed questionnaire," American Journal of Public Health, American Public Health Association, vol. 70(8), pages 823-825.
    2. Angela M. Wood & Ian R. White & Matthew Hotopf, 2006. "Using number of failed contact attempts to adjust for non‐ignorable non‐response," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(3), pages 525-542, July.
    3. Jiming Jiang & P. Lahiri, 2006. "Mixed model prediction and small area estimation," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 15(1), pages 1-96, June.
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