Exponential Ratio Type Estimators In Stratified Random Sampling
ABSTRACT Kadilar and Cingi (2003) have introduced a family of estimators using
auxiliary information in stratified random sampling. In this paper, we propose
the ratio estimator for the estimation of population mean in the stratified
random sampling by using the estimators in Bahl and Tuteja (1991) and Kadilar
and Cingi (2003). Obtaining the mean square error (MSE) equations of the
proposed estimators, we find theoretical conditions that the proposed
estimators are more efficient than the other estimators. These theoretical
findings are supported by a numerical example.
Full-textDOI: · Available from: Rajesh Singh, Aug 13, 2015
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ABSTRACT: In this paper, we suggest an estimator using two auxiliary variables in stratified random sampling. The propose estimator has an improvement over mean per unit estimator as well as some other considered estimators. Expressions for bias and MSE of the estimator are derived up to first degree of approximation. Moreover, these theoretical findings are supported by a numerical example with original data. Key words: Study variable, auxiliary variable, stratified random sampling, bias and mean squared error.03/2014; 8(1). DOI:10.18187/pjsor.v8i1.161
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ABSTRACT: This is an eclectic tome of 100 papers in various fields of sciences, alphabetically listed, such as: astronomy, biology, calculus, chemistry, computer programming codification, economics and business and politics, education and administration, game theory, geometry, graph theory, information fusion, neutrosophic logic and set, non-Euclidean geometry, number theory, paradoxes, philosophy of science, psychology, quantum physics, scientific research methods, and statistics – containing 800 pages. It was my preoccupation and collaboration as author, co-author, translator, or co-translator, and editor with many scientists from around the world for long time. Many ideas from this book are to be developed and expanded in future explorations.01/2010;