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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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題名:	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
關鍵詞:	Bias-reducing estimators;regeneration;simulation;steadystate estimation
日期:	2004-01
上傳時間:	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
資料類型:	article
DOI 連結:	http://dx.doi.org/10.1145/1029174.1029175
DOI:	10.1145/1029174.1029175
顯示於類別:	[資訊管理學系] 期刊論文

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