Estimating Population Coefficient of Variation Using a Single Auxiliary Variable in Simple Random Sampling

Abstract

This paper proposes an improved estimation method for the population coefficient of variation, which uses information on a single auxiliary variable. The authors derived the expressions for the mean squared error of the proposed estimators up to the first order of approximation. It was demonstrated that the estimators proposed by the authors are more efficient than the existing ones. The results of the study were validated by both empirical and simulation studies.

Improved Estimators of Population Coefficient of Variation under Simple Random Sampling

“…Singh et al [7] proposed various improved and more enhanced estimators based on the arithmetic mean, geometric mean, and harmonic mean of these estimators. Singh and Mishra [8] proposed estimating the population CV using a single auxiliary variable. Audu et al [9] proposed difference cum ratio type estimators for estimating population CV under SRS and demonstrated that their estimators are more efficient than the existing estimators.…”

Testing the annual rainfall dispersion in Chaiyaphum, Thailand, by using confidence intervals for the coefficient of variation of an inverse gamma distribution

“…Calif and Soubdhan (2016) used the CV to measure the spatial and temporal correlation of global solar radiation. Singh and Mishra (2019) proposed an improved estimation method for the population coefficient of variation, which uses information on a single auxiliary variable. Thangjai and Niwitpong (2020) proposed confidence interval estimation for the ratio of CV of two log-normal distributions constructed using the Bayesian approach.…”