ARTICLE
ABSTRACT
The present paper deals with the robust prediction of finite population total under the superpopulation modelGR . The design is de-emphasized while developing these predictors under the superpopulation model and making comparison among all resistant estimators. The suggested proposals involve reweighed iterative algorithm for Robust Prediction. The discussion also involves the calculation of asymptotic bias and variance in terms of the influence function computed for these predictors. Two populations have been considered for simulation study to judge the performance of proposed predictors with conventional and model based existing alternatives.
KEYWORDS
Influence function, Prediction approach, M-estimator, Superpopulation models.
REFERENCES
CASSEL, C. M. and SARNDAL C. E. (1977). Foundation of Inference in Survey Sampling, New York : John Wiley.
CHAMBERS, R. L. (1986). Outlier Robust Finite Population Estimation. Journal of the American Statistical Association, 81, 1063–1069.
ERNST, L.R(1980). Comparison of estimator of the Mean which Adjust for large Observations, 'Sankhya, Ser.C.42, 1–16.
FULLAR, W.A.(1991). Simple estimators of the mean of Skewed Populations, Statistica Sinica, 1, 137–158.
GLASSER, G.J.(1962). On the complete coverage of large units in a statistical study. International Statistical Review, 30, 28–32.
GWET, J. P. and RIVEST, L.P. (1992). Outlier resistant alternatives to the ratio estimator. Journal of the American Statistical Association, 87, 1174–1182.
HAMPEL, F.R., RONCHETTI, E.M., ROUSSEEUW, P.J. and STAHEL, W.E. (1986): Robust Statistics: The Approach Baaed on Influence Functions, New york: John Wiley.
HIDIROGIOU, M. H., and SRINATH, K. P. (1981): Some Estimators of the Population Total from simple Random Samples Containing Large Units, Journal of the American Statistical Association, 76, 690–695.
HULLIGER, B. (1995): Outlier Robust Horvitz-Thompson Estimators. Survey Methodology 21(1), 79–87.
ICHAN, R.(1984): Sampling strategies, robustness and efficiency: the state of art. International Statistical Review, 52, 209–218.
KISH, L. (1965). Survey Sampling, New York : John Wiley.
RAO, J.N.K (1985). Conditional Inferences in Survey Sampling, Survey Methodology, 11, 15–31.
RIVEST, L.P. (1993). Winsorization of Survey Data, Proceedings of the 49th Session, International Statistical Institute.
SCOTT, A. J., and BREWER, K. R. W., and HO, E. W. H. (1978). Finite Population Sampling and Robust Estimation, Journal of the American Statistical Association, 73 , 359–361.
SEARLS, D.T. (1966). An estimator for a population mean which reduces the effect of large observations, Journal of the. American Statistical Association, 61, 1200–1204.
SERFLING, R. J. (1980). Approximation Theorems of Mathematical Statistics, New York : John Wiley.
SHOEMAKER, L.H. and ROSENBERGER, J.L. (1983). Moments and efficiency of the median and trimmed mean for finite population, Communication in Statistics, Simulations and computation, 12(4), 411–422.
SMITH, T.M.F. (1987). Influential observation in survey sampling, Journal of Applied Statistics, 14,143–152.
SUKHATME, P. V., and SUKHATME, B. V. and ASOK, C. (1984). Sampling Theory of Surveys With Applications (2nd ed.), Rome: Food and Agriculture: Organization.
SARNDAL, C. E. and SWENSSON, J. W.(1991). Model Assisted Survey Sampling, Springer-Verlag, New York.
