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Weighting Simulation Data To Reduce Initialization Effects

 

作者: Mark Snell,   Lee Schruben,  

 

期刊: IIE Transactions  （Taylor Available online 1985）
卷期: Volume 17, issue 4  

页码: 354-363

 

ISSN:0740-817X

 

年代: 1985

 

DOI:10.1080/07408178508975315

 

出版商: Taylor & Francis Group

 

数据来源: Taylor

 

摘要:

A popular method for reducing initialization bias in simulation output is to delay the collection of data until the model has “warmed up”. This technique, called data truncation, is considered as a special case of observation weighting. Using a simple autoregressive model for the simulated series, several optimal weighting schemes are studied.

 

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