• 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