1 Affiliation : Department of Mathematical Science, Islamic University of Science and Technology, Jammu and Kashmir, India
Email : firstname.lastname@example.org
2 Affiliation : Department of Mathematical Science, Islamic University of Science and Technology, Jammu and Kashmir, India
Email : email@example.com
3 Affiliation : Department of Mathematical Science, Islamic University of Science and Technology, Jammu and Kashmir, India
Email : firstname.lastname@example.org
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.
Estimation; Mean Square error; Bias; Auxiliary variable; RRM.
AMS Subject Classification: 62D05.
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