Eideh Abdulhakeem

(English) PDF


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).


response distribution, nonignorable nonresponse, informative sampling design


NONRESPONSE and WEIGHTING ADJUSTMENTS: A Critical Review, Journal of Official Statistics 29, pp. 329–353.

CHAMBERS, R. L., CLARK, R. G., (2012). An Introduction to Model-Based Survey Sampling with Applications, Oxford Statistical Science Series.

EIDEH, A. H., (2016). Estimation of Finite Population Mean and Superpopulation Parameters when the Sampling Design is Informative and Nonresponse Mechanism is Nonignorable. Pakistan Journal of Statistics and Operation Research (PJSOR), Pak.j.stat.oper.res. Vol. XII No. 3, 2016, pp. 467–489.

EIDEH A. H., (2012). Estimation and Prediction under Nonignorable Nonresponse via Response and Nonresponse Distributions, Journal of Indian Society of Agriculture Statistics., 66(3) 2012, pp 359–380.

EIDEH A. H., (2009). On the use of the sample distribution and sample likelihood for inference under informative probability sampling. DIRASAT (Natural Science), Volume 36 (2009), Number 1, pp18–29.

EIDEH, A. H., (2007). Method of Moments Estimators of Finite Population Parameters in Case of Full Response and Nonresponse, Contributed Paper for the 56th Biennial Session of the International Statistical Institute, August 22–29, 2007, Lisboa, Portugal, ISI 2007 Book of Abstracts, p 430.

EIDEH, A. H., NATHAN, G., (2006). Fitting time series models for longitudinal survey data under informative sampling, Journal of Statistical Planning and Inference 136, 9, pp 3052– 306, [Corrigendum, 137 (2007), p 628].

LITTLE, R. J. A., (1983). Superpopulation models for nonresponse. In Incomplete Data in Sample Surveys, Vol. 2, Theory and bibliographies, part VI, pp. 337–413. New York, Academic Press.

LITTLE, R. J. A., (2003). Bayesian methods for unit and item nonresponse, In Analysis of survey data, Chambers R.L. and Skinner C.L., pp. 289–306, Wiley, New York.

LITTLE, R. J. A., RUBIN, D. B., (2002). Statistical analysis with missing data. New York: Wiley.

LITTLE, R.J.A., VARTIVARIAN, S., (2005). Does Weighting for Nonresponse Increase the Variance of Survey Means?, Survey Methodology, Vol. 31, No. 2, pp. 161–168.

PFEFFERMANN, D., KRIEGER, A. M., RINOTT, Y., (1998). Parametric distributions of complex survey data under informative probability sampling'. Statistica Sinica, 8, pp 1087– 1114.

PFEFFERMANN, D., SVERCHKOV, M., (1999). Parametric and semi-parametric estimation of regression models fitted to survey data, Sankhya, 61, B, pp 166–186.

PFEFFERMANN, D., SIKOV, A., (2011). Imputation and Estimation under Nonignorable Nonresponse in Household Surveys with Missing Covariate Information, Journal of Official Statistics, Vol. 27, No. 2, 2011, pp. 181–209.

RIDDLES, M. K., KIM, J. K., IM, J., (2016). A propensity-score-adjustment method for nonignorable nonresponse, J. Surv. Stat. Methodol., 4, pp. 215–245.

SÄRNDAL, C .E., (2011). The 2010 Morris Hansen lecture dealing with survey nonresponse in data collection, in estimation, Journal of Official Statistics, Vol. 27, No. 1, 2011, pp. 1–21.

SVERCHKOV, M., PFEFFERMANN, D., (2004). Prediction of finite population totals based on the sample distribution, Survey Methodology, 30, pp 79–92.

SVERCHKOV, M., (2008), A New Approach to Estimation of Response Probabilities When Missing Data Are Not Missing at Random, Proceedings of the Survey Research Methods Section, pp. 867–874.

Back to top
© 2019–2024 Copyright by Statistics Poland, some rights reserved. Creative Commons Attribution-ShareAlike 4.0 International Public License (CC BY-SA 4.0) Creative Commons — Attribution-ShareAlike 4.0 International — CC BY-SA 4.0