Variance Estimation Using Jackknife Method in Ranked Set Sampling under Finite Population Framework
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Date
2009
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Publisher
IARI, Indian Agricultural Statistics Research Institute
Abstract
In experimental settings where measuring an observation is expensive, but ranking a
small subset of observations is relatively easy, Ranked Set Sampling (RSS) can be
used to increase the precision of the estimators. The majority of research in RSS has
been concerned with estimating the mean are in the context of infinite population.
Estimating the variance in case of RSS has been found to be cumbersome in the
context of finite population. Therefore, in this study, an attempt was made to develop
variance estimation procedures using Jackknife method in RSS under finite
population framework. Under this study, three different variance estimation
procedures have been developed using Jackknife method in ranked set sampling under
finite population framework. The efficiency of these developed variance estimation
procedures has been compared among themselves through a simulation study. The
performance of variance estimation procedure following cycle based approach has
been found to be at par with strata based approach for varying number of cycles as
well as for varying ranks. The variance estimation procedures following cycle based
approach and strata based approach have performed better than the variance
estimation procedure following unit based approach for varying number of cycles as
well as for varying ranks.
Description
RFT-3169