Some new efficient linear regression ratio type estimators for estimating the population mean in sampling theory

Vinay Kumar Yadav; Shakti Prasad

doi:10.5269/bspm.68413

Vinay Kumar Yadav M S Ramaiah University of Applied Sciences https://orcid.org/0000-0003-2221-5873
Shakti Prasad National Institute of Technology Jamshedpur https://orcid.org/0000-0002-7867-7586

Resumen

This article deals with some new efficient linear regression ratio type estimators for estimating the population mean in sampling theory by using the auxiliary information of quartile deviation and deciles. The proposed estimators can be considered an efficient extension to the work of Kadilar and Cingi (Applied Mathematics and Computaton, 151, 893-902, 2004 \& Hacettepe Journal of Mathematics and Statistics, 35 (1), 103-109, 2006) and the Subjar (World Applied Sciences Journal, 35 (3), 377-384, 2017). The theoretical results are derived, and a comparative study is conducted. The suggested estimators are shown to have smaller mean squared errors than the Kadilar and Cingi (2004 \& 2006) and Subzar (2017) estimators. The percent relative efficiencies of the suggested estimators for various sample sizes are involved in simulation studies for a given natural population data set, and the results are found to be quite encouraging, providing an improvement over all previous work.

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Biografía del autor/a

Vinay Kumar Yadav, M S Ramaiah University of Applied Sciences

Department of Data Sciences and Analytics, School of Social Sciences

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