TY - JOUR
T1 - Mean estimate in ranked set sampling using a length-biased concomitant variable
AU - Cui, Chang
AU - Li, Tao
AU - Zhang, Lei
N1 - Publisher Copyright:
© 2018, © 2018 Taylor & Francis Group, LLC.
PY - 2019/6/18
Y1 - 2019/6/18
N2 - In this paper, a ranked set sampling procedure with ranking based on a length-biased concomitant variable is proposed. The estimate for population mean based on this sample is given. It is proved that the estimate based on ranked set samples is asymptotically more efficient than the estimate based on simple random samples. Simulation studies are conducted to present the properties of the proposed estimate for finite sample size. Moreover, the consequence of ignoring length bias is also addressed by simulation studies and the real data analysis.
AB - In this paper, a ranked set sampling procedure with ranking based on a length-biased concomitant variable is proposed. The estimate for population mean based on this sample is given. It is proved that the estimate based on ranked set samples is asymptotically more efficient than the estimate based on simple random samples. Simulation studies are conducted to present the properties of the proposed estimate for finite sample size. Moreover, the consequence of ignoring length bias is also addressed by simulation studies and the real data analysis.
KW - concomitant variable
KW - length-biased
KW - mean estimate
KW - ranked set sample
UR - http://www.scopus.com/inward/record.url?scp=85057553605&partnerID=8YFLogxK
U2 - 10.1080/03610926.2018.1473594
DO - 10.1080/03610926.2018.1473594
M3 - Article
AN - SCOPUS:85057553605
SN - 0361-0926
VL - 48
SP - 2917
EP - 2931
JO - Communications in Statistics - Theory and Methods
JF - Communications in Statistics - Theory and Methods
IS - 12
ER -