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Parameters Estimation in a Simple Linear Regression Using Rank Set Sampling
Author(s) -
Muttlak Hassen A.
Publication year - 1995
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.4710370704
Subject(s) - rss , simple random sample , estimator , statistics , simple linear regression , mathematics , sampling (signal processing) , simple (philosophy) , linear regression , set (abstract data type) , rank (graph theory) , regression , sampling design , computer science , combinatorics , population , philosophy , demography , filter (signal processing) , epistemology , sociology , computer vision , programming language , operating system
Ranked set sampling (RSS) as suggested by MCINTYRE (1952) and TAKAHASI and WAKIMOTO (1968) may be used to estimate the parameters of the simple regression line. The objective is to use the RSS method to increase the efficiency of the estimators relative to the simple random sampling (SRS) method. Estimators of the slope and intercept are considered. Computer simulated results are given, and an example using real data presented to illustrate the computations.