Paper Info
Title
New Estimator For The Variances Of Strata In Ranked Set Sampling
Abstract
Ranked set sampling (RSS) utilizes imprecise rankings on the variable of interest in order to draw an informative sample from the target population. The resulting sample, consisting of independent judgment order statistics, resembles a stratified random sample. Estimating the variances of strata is an important problem in RSS. The standard method is based on the sample variance of units in each stratum. A plug-in estimator is also available in the literature that remedies some shortcomings of the standard estimator. We adjust the latter estimator using kernel estimator of the distribution function. The developed estimator is shown to be consistent, and its performance is investigated by means of simulation. It turns out that our proposal can be considerably more efficient than the existing estimators when perfect or nearly perfect ranking holds.
Year
DOI
Venue
2021
10.1007/s00500-021-05787-1
SOFT COMPUTING
Keywords
DocType
Volume
Judgment ranking, Smoothing, Variance estimation
Journal
25
Issue
ISSN
Citations 
13
1432-7643
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
mahdi mahdizadeh111.76
Ehsan Zamanzade202.37