A Family of Estimators for Population Mean Under Model Approach in Presence of Non-Response
DOI:
https://doi.org/10.13052/jrss0974-8024.1511Keywords:
Non-response, families of estimators, polynomial regression model, mean square errorAbstract
We have defined a class of estimators for population mean under non-response error based upon the concept of sub-sampling of non-respondents utilizing an auxiliary variable. The class is a one-parameter class of estimators which is based on the idea of exponential type estimators (ETE). The model biasness and model-mean square error of the class and some of its important members have been derived under polynomial regression model (PRM). The effect of variations in PRM specifications on the efficiency of the estimators has been discussed based upon the empirical results.
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Bahl, S. Tuteja, R. K. (1991): Ratio and product type-exponential estimator, Information and Optimization Sciences, XII, I, 159–163.
Basu, D. (1958): On sampling with and without replacement, Sankhya, 20 A, 287–294.
Brewer, K. R. W. (1963): Ratio estimation and finite populations: Some results deducible from the assumption of an underlying stochastic process, Australian Journal of Statistics, 5, 93–105.
Cassel, C. M., Sarndal, C. E. and Wretman, J. H. (1976): Some results on generalized difference estimation and generalized regression estimation for finite populations, Biometrika, 63, 615–620.
Chambers, R.L. (1986): Outlier robust finite population estimation, Journal of the American Statistical Association, 81(396), 1063–1069.
Cochran, W. G. (1953): Sampling Techniques, John Wiley and Sons, Inc., New York, I Edition.
Deming, W. E. (1944): On errors in surveys, American Sociological Review, 9, 359–369.
Hansen, M. H. and Hurwitz, W. N. (1946): The problem of non-response in sample surveys, Journal of the American Statistical Association, 41, 517–529.
Kish, L. (1967): Survey Sampling. John Wiley and Sons, Inc., New York, II Edition.
Mahalanobis, P. C. (1946): Recent experiments in statistical sampling in The Indian Statistical Institute, Journal of The Royal Statistical Society, 109A, 325–378.
Moser, C. A. (1958): Survey Methods in Social Investigation, Heinemann, London.
Royall, R. M. (1971): Linear regression models in finite population sampling theory, in Foundations of Statistical Inference, V. P. Godambe and D. A. Sprott (eds.), Toronto: Holt, Rinehart and Winston, 259–274.
Royall, R. M. and Herson, J. (1973a): Robust estimation in finite populations I, Journal of the American Statistical Association, 68(344), 880–889.
Royall, R. M. and Herson, J. (1973b): Robust estimation in finite populations II: Stratification on a size variable, Journal of the American Statistical Association, 68(344), 890–893.
Singh H.P., Solanki, R. S. (2011): Estimation of finite population mean using random non-response in survey sampling, Pakistan Journal of Statistics and Operation Research, 7(1), 21–41.
Singh H.P., Solanki, R. S. (2011): A General procedure for estimating the population parameter in the presence of random non-response. Pakistan Journal of Statistics, 27(4), 427–465 (2011).
Singh, V. K., Singh, R. V. K. and Shukla, R. K. (2009b): Model-based study of some estimators in the presence of non-response, in Population, Poverty and Health: Analytical Approaches (Eds. K. K. Singh, R. C. Yadava and Arvind Pandey), Hindustan Publishing Corporation, New Delhi, India, 360–365.
Singh, A. K. Singh, Priyanka and Singh, V. K. (2017): Model based study of families of exponential type estimators in presence of nonresponse, Communications in Statistics – Theory and Methods, 46,13, 6478–6490.
Shukla, R. K. (2010): Model-Based Efficiencies of Some Families of Estimators in Presence of Non-Response and Measurement Errors, Unpublished, Ph.D. Thesis submitted to Banaras Hindu University, Varanasi, India.
Zarkovich, S. S. (1966): Quality of Statistical Data, Food and Agricultural Organization of the United Nations, Rome.