Estimation of Population Mean Using Information on Two Auxiliary Attributes

Kumar, Neelesh; Singh, Housila P.

doi:10.1134/s1995080223020221

Cited by 2 publications

(1 citation statement)

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“…There arise situations when the supplementary information exists in attributes form, which is correlated highly with the variable under study. Taking into account, relationship between the study variable and supplementary characteristic, many authors including Naik and Gupta [13], Lone et al [11], Kumar and Singh [9] and Rather et al [14] paid attention by using ratio estimators to improve the estimation of the population parameter. Singh et al [16] developed several populations mean estimators of the variable under study with the help of the population proportion of the supplementary attribute.…”

Section: Introductionmentioning

confidence: 99%

Improved Mean Estimators for Population utilizing Dual Supplementary Characteristics under Simple Random Sampling

Hussain,

Zaman,

Khan

et al. 2024

VFAST trans. math.

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This paper makes another addition to the existing literature of population mean estimation. An improved family of mean estimators for the population is suggested using simple random sampling and utilizing the information of dual supplementary characteristics. We have conducted an extensive theoretical and numerical investigation of these recommended estimators using the criteria of bias and mean square error. The study provides a derivation of the bias and mean square error of the recommended estimators, approximating up to the first order and compared with the existing estimators. The suggested estimators are also extensively studied numerically and compared using real-world data sets and simulation studies. The theoretical and numerical comparisons show that the estimators suggested in the study are much more efficient than the competitor estimators under all situations i.e. Bahl and Tuteja [21] and other existing estimators in the literature. Therefore, the suggested family of estimators can be applied to real-life problems to obtain better results than the existing mean estimators for the population.

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Section: Introductionmentioning

confidence: 99%