A procedure for selecting a subset of size m containing the l best of k independent normal populations, with applications to simulation. Koenig, L. W. & Law, A. M. Communications in Statistics - Simulation and Computation, 14(3):719-734, 1985.
Paper doi abstract bibtex In this paper we state and justify a two-stage sampling procedure for selecting a subset of size m containing the t best of k independent normal populations, when the ranking parameters are the population means. We do not assume that the variances of the populations are known or equal. Discrete event simulation studies are often concerned with choosing one or more system designs which are best in some sense. We present empirical results from a typical simulation application for which the observations are not normally distributed.
@article{Koenig+Law:1985,
author = { Lloyd W. Koenig and Averill M. Law },
title = {A procedure for selecting a subset of size m containing the l best of k independent normal populations, with applications to simulation},
journal = {Communications in Statistics - Simulation and Computation},
volume = {14},
number = {3},
pages = {719-734},
year = {1985},
doi = {10.1080/03610918508812467},
URL = {http://dx.doi.org/10.1080/03610918508812467},
eprint = {http://dx.doi.org/10.1080/03610918508812467} ,
abstract = {In this paper we state and justify a two-stage sampling procedure for selecting a subset of size m containing the t best of k independent normal populations, when the ranking parameters are the population means. We do not assume that the variances of the populations are known or equal. Discrete event simulation studies are often concerned with choosing one or more system designs which are best in some sense. We present empirical results from a typical simulation application for which the observations are not normally distributed.},
bib2html_rescat = {Bandits}
}
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