DocumentCode :
2178334
Title :
Restricted subset selection
Author :
Chen, E. Jack
Author_Institution :
BASF Corp., Rockaway, NJ, USA
fYear :
2008
fDate :
7-10 Dec. 2008
Firstpage :
281
Lastpage :
289
Abstract :
This paper develops procedures for selecting a set of normal populations with unknown means and unknown variances in order that the final subset of selected populations satisfies the following requirements: with probability at least P*, the selected subset will contain a population or ¿only and all¿ of those populations whose mean lies less than the distance d* from the smallest mean. The size of the selected subset is random, however, at most m populations will finally be chosen. A restricted subset attempts to exclude populations that are deviated more than d* from the smallest mean. Here P*, d*, and m are users specified parameters. The procedure can be used when the unknown variances across populations are unequal. An experimental performance evaluation demonstrates the validity and efficiency of these restricted subset selection procedures.
Keywords :
discrete event simulation; set theory; discrete-event simulation; normal populations set selection; restricted subset selection; Computational modeling; Control systems; Convergence; Discrete event simulation; Inference algorithms; Lifting equipment;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Simulation Conference, 2008. WSC 2008. Winter
Conference_Location :
Austin, TX
Print_ISBN :
978-1-4244-2707-9
Electronic_ISBN :
978-1-4244-2708-6
Type :
conf
DOI :
10.1109/WSC.2008.4736079
Filename :
4736079
Link To Document :
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