Novel logarithmic imputation procedures using multi auxiliary information under ranked set sampling
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Date
2024
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Abstract
Ranked set sampling (RSS) is known to increase the efciency of the estimators while comparing
it with simple random sampling. The problem of missingness creates a gap in the information that
needs to be addressed before proceeding for estimation. Negligible amount of work has been carried
out to deal with missingness utilizing RSS. This paper proposes some logarithmic type methods
of imputation for the estimation of population mean under RSS using auxiliary information. The
properties of the suggested imputation procedures are examined. A simulation study is accomplished
to show that the proposed imputation procedures exhibit better results in comparison to some of the
existing imputation procedures. Few real applications of the proposed imputation procedures is also
provided to generalize the simulation study.