Estimation of population mean and variance in flock management: a ranked set sampling approach in a finite population setting

2005 ◽  
Vol 75 (11) ◽  
pp. 905-919 ◽  
Author(s):  
Omer Ozturk ◽  
Omer C. Bilgin ◽  
Douglas A. Wolfe
2021 ◽  
Vol 8 (1) ◽  
pp. 1948184
Author(s):  
Asad Ali ◽  
Muhammad Moeen Butt ◽  
Kanwal Iqbal ◽  
Muhammad Hanif ◽  
Muhammad Zubair

1969 ◽  
Vol 13 (2) ◽  
pp. 117-126 ◽  
Author(s):  
Derek J. Pike

Robertson (1960) used probability transition matrices to estimate changes in gene frequency when sampling and selection are applied to a finite population. Curnow & Baker (1968) used Kojima's (1961) approximate formulae for the mean and variance of the change in gene frequency from a single cycle of selection applied to a finite population to develop an iterative procedure for studying the effects of repeated cycles of selection and regeneration. To do this they assumed a beta distribution for the unfixed gene frequencies at each generation.These two methods are discussed and a result used in Kojima's paper is proved. A number of sets of calculations are carried out using both methods and the results are compared to assess the accuracy of Curnow & Baker's method in relation to Robertson's approach.It is found that the one real fault in the Curnow-Baker method is its tendency to fix too high a proportion of the genes, particularly when the initial gene frequency is near to a fixation point. This fault is largely overcome when more individuals are selected. For selection of eight or more individuals the Curnow-Baker method is very accurate and appreciably faster than the transition matrix method.


2003 ◽  
Vol 54 (1-2) ◽  
pp. 105-114
Author(s):  
Sukuman Sarikavanij ◽  
Montip Tiensuw

In this paper we discuss two case studies which clearly indicate the advantages of using a ranked set sample (RSS) over those of a simple random sample (SRS). The applications of RSS considered here cover single family homes sales data, and tree data. It is demonstrated that in each case RSS is much more efficient than SRS for estimation of population mean.


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