Parametric prediction of finite population total under Informative sampling and nonignorable nonresponse


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Statistics in Transition New Series

Polish Statistical Association

Central Statistical Office of Poland

Subject: Economics , Statistics & Probability


ISSN: 1234-7655
eISSN: 2450-0291





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VOLUME 21 , ISSUE 1 (March 2020) > List of articles

Parametric prediction of finite population total under Informative sampling and nonignorable nonresponse

Abdulhakeem Eideh

Keywords : response distribution, nonignorable nonresponse, informative sampling design

Citation Information : Statistics in Transition New Series. Volume 21, Issue 1, Pages 13-35, DOI:

License : (CC BY-NC-ND 4.0)

Received Date : 09-July-2019 / Accepted: 04-February-2020 / Published Online: 18-March-2020



In this paper, we combine two methodologies used in the model-based survey sampling, namely the prediction of the finite population total, named T, under informative sampling and full response, see Sverchkov and Pfeffermann  (2004), and the prediction of T with a noninformative sampling design and the nonignorable nonresponse mechanism, see Eideh (2012). The former approach involves the dependence of the first order inclusion probabilities on the study variable, while the latter involves the dependence of the probability of nonresponse on unobserved or missing observations. The main aim of the paper is to consider how to account for the joint effects of informative sampling designs and notmissing-at-random response mechanism in statistical models for complex survey data. For this purpose, theoretically, we use the response distribution and relationships between the moments of the superpopoulation, the sample, sample-complement, response, and nonresponse distributions for the prediction of finite population totals, see Eideh (2016). The derived parametric predictors of T use the observation for the response set of the study variable or variable of interest, values of auxiliary variables and their population totals, sampling weights, and propensity scores. An interesting outcome of the T study is that most predictors known from model-based survey sampling can be derived as a special case from this general theory, see Chambers and Clark (2012).

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