A procedure for selecting a subset of size m containing the l best of k independent normal populations, with applications to simulation

Koenig, Lloyd W.; Law, Averill M.

doi:10.1080/03610918508812467

Cited by 113 publications

(85 citation statements)

References 4 publications

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“…We can extend to multi-stage procedures (sequential procedures) assuming common, known variances by defining the user-specified number of observations. Bechhofer et al [1] presented such methodologies and Koeing and Law [22] extended the indifference zone approach for sifting procedure. As contrasted with the articles discussed, Frey and Dueck [10] presented a representative examplar procedure not requiring reduction to a univariate model.…”

Section: Statistical Selection Methods and Random Search Methodsmentioning

confidence: 99%

Optimized combinatorial clustering for stochastic processes

Kim¹,

Lee

Song

et al. 2017

Cluster Comput

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As a new data processing era like Big Data, Cloud Computing, and Internet of Things approaches, the amount of data being collected in databases far exceeds the ability to reduce and analyze these data without the use of automated analysis techniques, data mining. As the importance of data mining has grown, one of the critical issues to emerge is how to scale data mining techniques to larger and complex databases so that it is particularly imperative for computationally intensive data mining tasks such as identifying natural clusters of instances. In this paper, we suggest an optimized combinatorial clustering algorithm for noisy performance which is essential for large data with random sampling. The algorithm outperforms conventional approaches through various numerical and qualitative thresholds like mean and standard deviation of accuracy and computation speed.

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Section: Statistical Selection Methods and Random Search Methodsmentioning

confidence: 99%

Optimized combinatorial clustering for stochastic processes

Kim¹,

Lee

Song

et al. 2017

Cluster Comput

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“…Consider the (s, S) inventory system for the k = 5 inventory policies provided in Koenig and Law (1985). The objective of the study is to compare for the five policies given in Table 5 on the basis of their corresponding expected average cost per month for the first 30 months of operation and select the policy with the minimum expected cost.…”

Section: An Illustrative Examplementioning

confidence: 99%

The tradeoff between sampling and switching: New sequential procedures for indifference-zone selection

Nelson

2005

IIE Transactions

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Statistical ranking and selection (R&S) is a collection of experiment design and analysis techniques for selecting the "population" with the largest or smallest mean performance from among a finite set of alternatives. R&S procedures have received considerable research attention in the stochastic simulation community, and they have been incorporated in commercial simulation software. One of the ways that R&S procedures are evaluated and compared is via the expected number of samples (often replications) that must be generated to reach a decision. In this paper we argue that sampling cost alone does not adequately characterize the efficiency of ranking-and-selection procedures, the cost of switching among the simulations of the alternative populations should also be considered. We introduce two new, adaptive procedures, Minimum Switching Sequential Procedure (MSS) and Multi-stage Sequential Procedure with Tradeoff (MST), that provide the same statistical guarantees as existing procedures and significantly reduce the expected total computational cost of application, especially when applied to favorable configurations of the competing means.

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“…One is to maximize the probability of choosing the top m systems (Chen, He, Fu, and Lee 2008), while others wish to choose a subset of size m that contains some number of the best systems (Koenig and Law 1985). In Ryzhov and Powell (2009), possible subsets are treated as individual alternatives and sampling rules applied to choose the best subset.…”

Section: Literature Reviewmentioning

confidence: 99%

Multiple comparisons with a standard using false discovery rates

Singham¹,

Szechtman²

2016

2016 Winter Simulation Conference (WSC)

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Multiple comparisons with a standard using false discovery rates Downloaded from NPS Archive: CalhounProceedings of the 2016 Winter Simulation Conference T. M. K. Roeder, P. I. Frazier, R. Szechtman, E. Zhou, T. Huschka, and S. E. Chick, eds. MULTIPLE COMPARISONS WITH A STANDARD USING FALSE DISCOVERY RATES ABSTRACTWe introduce a new framework for performing multiple comparisons with a standard when simulation models are available to estimate the performance of many different systems. In this setting, a large proportion of the systems have mean performance from some known null distribution, and the goal is to select alternative systems whose means are different from that of the null distribution. We employ empirical Bayes ideas to achieve a bound on the false discovery rate (proportion of selected systems from the null distribution) and a desired probability an alternate type system is selected.

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A procedure for selecting a subset of size m containing the l best of k independent normal populations, with applications to simulation

Cited by 113 publications

References 4 publications

Optimized combinatorial clustering for stochastic processes

Optimized combinatorial clustering for stochastic processes

The tradeoff between sampling and switching: New sequential procedures for indifference-zone selection

Multiple comparisons with a standard using false discovery rates

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