On multistage ranked set sampling for distribution and median estimation

A variation of ranked set sampling (RSS), multistage RSS (MSRSS), is investigated for the estimation of the distribution function and some of its quantiles, in particular the median. It is shown that this method is significantly more efficient than simple random sampling (SRS). The method becomes mo...

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Bibliographic Details
Published inComputational statistics & data analysis Vol. 52; no. 4; pp. 2066 - 2078
Main Authors Al-Saleh, Mohammad Fraiwan, Samuh, Monjed Hisham
Format Journal Article
LanguageEnglish
Published Amsterdam Elsevier B.V 10.01.2008
Elsevier Science
Elsevier
SeriesComputational Statistics & Data Analysis
Subjects
Distribution theory
Empirical distribution function
Exact sciences and technology
General topics
Mathematics
Multistage ranked set sampling
Multivariate analysis
Numerical analysis
Numerical analysis. Scientific computation
Numerical methods in probability and statistics
Probability and statistics
Probability theory and stochastic processes
Ranked set sampling
Sciences and techniques of general use
Simple random sampling
Statistics
Empirical distribution function
Simple random sampling
Ranked set sampling
Multistage ranked set sampling
Data analysis
Random sampling
Median
Multistage sampling
Sampling distribution
Function estimation
Statistical computation
Distribution function
Quantile
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Summary:A variation of ranked set sampling (RSS), multistage RSS (MSRSS), is investigated for the estimation of the distribution function and some of its quantiles, in particular the median. It is shown that this method is significantly more efficient than simple random sampling (SRS). The method becomes more and more effective as the number of stages r increases. Two estimators of the median based on MSRSS are proposed and compared to the sample median obtained by SRS.
ISSN:0167-9473
1872-7352
DOI:10.1016/j.csda.2007.07.002