An innovative additive mathematical model using auxiliary information

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An innovative additive mathematical model using auxiliary information

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Abstract

This article proposes innovative ratio and regression estimators based on additive randomized response model. Expressions for the biases and mean squared errors of the recommended estimators are derived. It has been revealed that the advised groundbreaking ratio and regression estimators are improved than ratio and regression estimators under a very realistic condition. Numerical illustrations and simulation study are also given in support of the present study.

Keywords

Estimation Mean Square error Bias Auxiliary variable RRM.

References

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