var-help: Variance estimation based on ranked set sampling

In RSSampling: Ranked Set Sampling

Description

The varRSS function estimates the variance based on ranked set sampling as types of Stokes or Montip&Sukuman.

Usage

  varRSS(X,m,r,type)

Arguments

X	An obtained ranked set sample
m	Size of units in each set
r	Number of cycles
type	character string, one of "Stokes" or "Montip".

Details

An obtained ranked set sample X must be m by r matrix. Stokes (1980) showed that estimator for variance is biased. Montip and Sukuman(2003) showed that for one cycle there is no unbiased estimator for variance but for more than one cycle they proposed unbiased estimator for variance.

Value

var	the estimated population variance based on ranked set sampling

References

Al-Hadhrami, S.A. (2010). "Estimation of the Population Variance Using Ranked Set Sampling with Auxiliary Variable". Int. J. Contemp. Math. Sciences, Vol. 5, no. 52, 2567 - 2576.

Stokes, S.L. (1980). "Estimation of Variance Using Judgment Ordered Ranked Set Samples". Biometrics, Vol. 36, No. 1, pp. 35-42.

Examples

 data=rnorm(10000,2,1)
 samplerss=rss(data,m=4,r=3,sets=FALSE)
 ## Estimation of variance based on ranked set sample by Stokes
 varRSS(samplerss,m=4,r=3,type="Stokes")
  ## Estimation of variance based on ranked set sample by Montip&Sukuman
 varRSS(samplerss,m=4,r=3,type="Montip")

RSSampling documentation built on May 2, 2019, 4:28 a.m.