RSSampling (version 1.0)

obsno.Mrss: observation numbers based on classical and modified ranked set sampling methods

Description

The obsno.Mrss function gives the observation numbers to sample from a target population by using modified ranked set sampling methods. Ranking is done using the concomitant variable Y.

Usage

obsno.Mrss(Y,m,r=1,type="r",p)

Arguments

Y

A vector of concomitant variable from target population

m

Size of units in each set

r

Number of cycles

type

type of the modified RSS method. "r" for traditional RSS, "p" for Percentile RSS, "m" for Median RSS, "bg" for Balanced Groups RSS, "e" for Extreme RSS. Default value is "r"

p

Value of percentile for Percentile RSS method

Details

Concomitant variable Y must be a vector.

References

McIntyre, G. A. (1952). A method for unbiased selective sampling, using ranked sets. Australian Journal of Agricultural Research, 3(4), 385-390.

Dell, T. R., & Clutter, J. L. (1972). Ranked set sampling theory with order statistics background. Biometrics, 28, 545-553.

Samawi, H. M., Ahmed, M. S., & Abu-Dayyeh, W. (1996). Estimating the population mean using extreme ranked set sampling. Biometrical Journal, 38(5), 577-586.

Muttlak, H. A. (1997). Median ranked set sampling. Journal of Applied Statistical Sciences, 6(4), 245-255.

Muttlak, H. A. (2003). Modified ranked set sampling methods. Pakistan Journal Of Statistics, 19(3), 315-324.

Jemain, A. A., Al-Omari, A., & Ibrahim, K. (2008). Some variations of ranked set sampling. Electronic Journal of Applied Statistical Analysis, 1(1), 1-15.

See Also

con.Mrss, Mrss, rss

Examples

# NOT RUN {
  y=rexp(10000)
  ## Determining the observation numbers of the units which are chosen to sample

  y=rexp(10000)
  obsno.Mrss(y,m=3,r=5)
  obsno.Mrss(y,m=5,r=6,type="m")
  obsno.Mrss(y,m=7,r=3,type="e")
  obsno.Mrss(y,m=4,r=5,type="p",p=0.3)
  obsno.Mrss(y,m=6,r=2,type="bg")
# }