Rescaling bootstrap technique for variance estimation for ranked set samples in finite population

Ranked Set Sampling (RSS) is preferred to Simple Random Sampling (SRS) when measuring an observation is expensive or time-consuming, while ranking small subset of observations is relatively easy. Estimating the variance of RSS estimator has been found cumbersome under finite population. In this study, we propose two rescaling bootstrap variance estimation techniques in RSS under finite population framework viz. Strata Based Rescaling Bootstrap (SBRB) and Cluster Based Rescaling Bootstrap (CBRB) methods. Simulation as well as real data application results suggest that SBRB method performs better than CBRB method for different combination of set size (m) and number of cycles (r).