LINEAR ALLOCATION MODELS FOR SYMMETRIC DISTRIBUTION IN RANKED SET SAMPLING
Keywords:
Ranked Set Sampling, Relative Precision, Neyman’s allocation, KPT Model, Ordered Statistics.Abstract
Ranked Set Sampling (RSS) is one method to potentially increase precision and reduce cost by using simple judgment or qualitative information. For symmetric distributions, an optimal allocation model was suggested by Kaur et al. (1995) (for simplicity in notation we call it by KPT model). This allocation model measures either only mid or extreme rank orders. This results in an estimator, which is not sufficient and hence unreliable in most of the situations, although it is more precise then Neyman’s allocation.
In this paper, we have proposed a Linear allocation model for two classes of symmetric distributions. These two classes of symmetric distribution are mound shaped and U-shaped, depending upon the plots of the variances of the order statistics against the rank order. The proposed allocation model is opposite to the Neyman allocation model and has an advantage over KPT model in the sense that measurements are made upon each rank orders
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