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2017•Hacettepe Journal of Mathematics and StatisticsOpen access

A new class of unbiased linear estimators in systematic sampling

Eda Gizem Koçyiğit, Hülya Çıngı

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Abstract

Use of auxiliary variables is very common in estimating various population parameters. In this study, we suggest a class of unbiased linear estimators for estimating the population mean of the study variate y using information on the auxiliary variate x in systematic sampling. The variance expressions of the suggested estimators are compared with usual unbiased estimator, Swain's (1964) ratio estimator and Shukla's (1971) product type estimator. It is demonstrated that the proposed estimators are more efficient than others.

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What this paper is about

Use of auxiliary variables is very common in estimating various population parameters. In this study, we suggest a class of unbiased linear estimators for estimating the population mean of the study variate y using information on the auxiliary variate x in systematic sampling. The variance expressions of the suggested estimators are compared with usual unbiased estimator, Swain's (1964) ratio estimator and Shukla's (1971) product type estimator. It is demonstrated that the proposed estimators are more efficient than others.

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Available abstract

Use of auxiliary variables is very common in estimating various population parameters. In this study, we suggest a class of unbiased linear estimators for estimating the population mean of the study variate y using information on the auxiliary variate x in systematic sampling. The variance expressions of the suggested estimators are compared with usual unbiased estimator, Swain's (1964) ratio estimator and Shukla's (1971) product type estimator. It is demonstrated that the proposed estimators are more efficient than others.

Key concepts: Mathematics, Best linear unbiased prediction, Estimator, Class (philosophy), Bias of an estimator, Statistics, Systematic sampling, Unbiased Estimation

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