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Parametric estimation for the location parameter for symmetric distributions using moving extremes ranked set sampling with application to trees data
Author(s) -
AlSaleh Mohammad Fraiwan,
AlHadrami Said Ali
Publication year - 2003
Publication title -
environmetrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.68
H-Index - 58
eISSN - 1099-095X
pISSN - 1180-4009
DOI - 10.1002/env.610
Subject(s) - estimator , statistics , mathematics , ranking (information retrieval) , rss , sampling (signal processing) , simple random sample , parametric statistics , set (abstract data type) , computer science , artificial intelligence , population , demography , filter (signal processing) , sociology , computer vision , operating system , programming language
A modification of ranked set sampling (RSS) called moving extremes ranked set sampling (MERSS) is considered parametrically, for the location parameter of symmetric distributions. A maximum likelihood estimator (MLE) and a modified MLE are considered and their properties are studied. Their efficiency with respect to the corresponding estimators based on simple random sampling (SRS) are compared for the case of normal distribution. The method is studied under both perfect and imperfect ranking (with error in ranking). It appears that these estimators can be real competitors to the MLE using (SRS). The procedure is illustrated using tree data. Copyright © 2003 John Wiley & Sons, Ltd.