Generalized ratio-type and ratio-exponential-type estimators for population mean under modified Horvitz-Thompson estimator in adaptive cluster sampling

ABSTRACT

In the present article, we propose the generalized ratio-type and generalized ratio-exponential-type estimators for population mean in adaptive cluster sampling (ACS) under modified Horvitz-Thompson estimator. The proposed estimators utilize the auxiliary information in combination of conventional measures (coefficient of skewness, coefficient of variation, correlation coefficient, covariance, coefficient of kurtosis) and robust measures (tri-mean, Hodges-Lehmann, mid-range) to increase the efficiency of the estimators. Properties of the proposed estimators are discussed using the first order of approximation. The simulation study is conducted to evaluate the performances of the estimators. The results reveal that the proposed estimators are more efficient than competing estimators for population mean in ACS under both modified Hansen-Hurwitz and Horvitz-Thompson estimators.

KEYWORDS:

• Adaptive cluster sampling (ACS)
• auxiliary variable
• coefficient of variation
• mean square error (MSE)

MATHEMATICS SUBJECT CLASSIFICATION:

• 62DO5

Acknowledgments

Authors are thankful to the Editors and the anonymous reviewers for their valuable suggestions, which helped to improve the research paper in its final form.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by Higher Education Commission, Pakistan.