Jirawan Jitthavech and Vichit Lorchirachoonkul

pp. 749 - 760

Abstract

This paper investigates four new estimators in simple random sampling, biased sample mean, ratio estimator, and two
linear regression estimators, using known coefficients of variation of the study variable and auxiliary variable. The properties
of the new estimators are obtained. Comparisons among the new estimators, three traditional estimators, Searls sample mean,
Koyuncu and Kadilar (KK) estimators, Sisodia and Dwivedi (SD) estimator, and Shabbir and Gupta (SG) estimator are undertaken. The analysis shows that the two proposed linear regression estimators are more efficient than the new biased sample
mean and at least as efficient as the three traditional estimators and SD estimator. At least one of the proposed linear regression
estimators is always more efficient than the new ratio estimator and Searls sample mean. From the numerical results using two
data sets published in the literature, the proposed linear regression estimators are clearly more efficient than all seven existing
estimators. 

Keywords

• estimator selection criteria
• linear regression estimator
• bias
• MSE
• relative efficiency
• simple random sampling