The present work emphasizes the role of several stable auxiliary variables at both the occasions to improve the precision of estimates at current occasion in two-occasion successive sampling. A chain-type multiple linear regressions in ratio estimator has been proposed and its theoretical properties are examined. Relative comparison of efficiencies of the proposed estimator with the sample mean estimator, when there is no matching from the previous occasion and the natural successive sampling estimator, when no auxiliary information is used have been made. Theoretical results have been well supported with empirical illustrations.
This paper defines a general class of estimators for estimating population variance on current occasion in two occasion successive sampling. Detail behaviors of the proposed class of estimators have been studied and its optimum replacement strategy has also been discussed. The proposed class of estimators has been compared with the sample variance estimator and the results obtained are demonstrated through empirical studies. Categorization of the dominance ranges of the proposed estimation strategies are deployed through defuzzification tools which are followed by suitable recommendations.
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