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    政大機構典藏 > 商學院 > 資訊管理學系 > 期刊論文 >  Item 140.119/27342


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    题名: Empirical Performance of Bias-Reducing Estimators for Regenerative Steady-State Simulations
    作者: 謝明華;D. Iglehart;P. Glynn
    Hsieh,Ming-Hua;D. Iglehart;P. Glynn
    Hsieh,Ming-Hua
    关键词: Bias-reducing estimators;regeneration;simulation;steadystate estimation
    日期: 2004-01
    上传时间: 2009-01-17 16:33:30 (UTC+8)
    摘要: When simulating a stochastic system, simulationists often are interested in estimating various steady-state performance measures. The classical point estimator for such a measure involves simply taking the time average of an appropriate function of the process being simulated. Since the simulation can not be initiated with the (unknown) steady-state distribution, the classical point estimator is generally biased. In the context of regenerative steady-state simulation, a variety of other point estimators have been developed in an attempt to minimize the bias. In this paper, we provide an empirical comparison of these estimators in the context of four different continuoustime Markov chain models. The bias of the point estimators and the coverage probabilities of the associated confidence intervals are reported for the four models. Conclusions are drawn from this experimental work as to which methods are most effective in reducing bias.
    關聯: ACM Transactions on Modeling and Computer Simulation, 14(4), 325-343
    数据类型: article
    DOI 連結: http://dx.doi.org/10.1145/1029174.1029175
    DOI: 10.1145/1029174.1029175
    显示于类别:[資訊管理學系] 期刊論文

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