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2015•Mathematical Journal of Interdisciplinary SciencesOpen access

A FAMILY OF UNBIASED MODIFIED LINEAR REGRESSION ESTIMATORS

J. Subramani

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Abstract

In this paper, a family of modified linear regression estimators has been proposed which are unbiased. The variance of the proposed estimators and the conditions for which the proposed estimators perform better than the classical ratio estimator and the existing modified ratio estimators have been obtained. Further, we have shown that the classical ratio estimator, the existing modified ratio estimators, and the usual linear regression estimator are the particular cases of the proposed estimators. It is observed from the numerical study that the proposed estimators perform better than the ratio estimator and the existing modified ratio estimators.

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What this paper is about

In this paper, a family of modified linear regression estimators has been proposed which are unbiased. The variance of the proposed estimators and the conditions for which the proposed estimators perform better than the classical ratio estimator and the existing modified ratio estimators have been obtained. Further, we have shown that the classical ratio estimator, the existing modified ratio estimators, and the usual linear regression estimator are the particular cases of the proposed estimators. It is observed from the numerical study that the proposed estimators perform better than the ratio estimator and the existing modified ratio estimators.

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Available abstract

In this paper, a family of modified linear regression estimators has been proposed which are unbiased. The variance of the proposed estimators and the conditions for which the proposed estimators perform better than the classical ratio estimator and the existing modified ratio estimators have been obtained. Further, we have shown that the classical ratio estimator, the existing modified ratio estimators, and the usual linear regression estimator are the particular cases of the proposed estimators. It is observed from the numerical study that the proposed estimators perform better than the ratio estimator and the existing modified ratio estimators.

Key concepts: Estimator, Minimum-variance unbiased estimator, Ratio estimator, Extremum estimator, Mathematics, Stein's unbiased risk estimate, Invariant estimator, Statistics

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