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2016•Equity Journal of Science and TechnologyRequires access

IMPROVED CLASS OF RATIO-PRODUCT TYPE ESTIMATORS BY USING UNKNOWN WEIGHT

Ran Vijay Singh, Ali Ahmed, G. I. Onwuka, Afolabi Wasiu Babayemi, Deepak Kumar

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Abstract

In this paper a class of ratio-type estimator has been proposed by using unknown constant to estimate population mean of the study variable. The proposed estimator includes a number of estimators. The particular cases of proposed estimator have been discussed. The expression for bias and the MSE of the proposed estimator were derived up to the first order of approximation. The efficiency of the proposed estimator has been compared with the estimators that are particular cases of proposed estimator and condition under which the proposed estimator is more efficient is derived. Appropriate Numerical illustrations are used to determine the efficiency of proposed estimator and existing estimators.

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What this paper is about

In this paper a class of ratio-type estimator has been proposed by using unknown constant to estimate population mean of the study variable. The proposed estimator includes a number of estimators. The particular cases of proposed estimator have been discussed. The expression for bias and the MSE of the proposed estimator were derived up to the first order of approximation. The efficiency of the proposed estimator has been compared with the estimators that are particular cases of proposed estimator and condition under which the proposed estimator is more efficient is derived. Appropriate Numerical illustrations are used to determine the efficiency of proposed estimator and existing estimators.

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Available abstract

In this paper a class of ratio-type estimator has been proposed by using unknown constant to estimate population mean of the study variable. The proposed estimator includes a number of estimators. The particular cases of proposed estimator have been discussed. The expression for bias and the MSE of the proposed estimator were derived up to the first order of approximation. The efficiency of the proposed estimator has been compared with the estimators that are particular cases of proposed estimator and condition under which the proposed estimator is more efficient is derived. Appropriate Numerical illustrations are used to determine the efficiency of proposed estimator and existing estimators.

Key concepts: Estimator, Invariant estimator, Efficient estimator, Mathematics, Trimmed estimator, Extremum estimator, Consistent estimator, Minimum-variance unbiased estimator

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