Incomplete Batch Effects in Simulation Output Analysis

• Main Authors: Ling-Hsiu Huang, 黃鈴琇
• Other Authors: Yingchieh Yeh
• Format: Others
• Language: zh-TW
• Published: 2004
• Online Access: http://ndltd.ncl.edu.tw/handle/25634815437999199755

碩士 === 元智大學 === 企業管理學系 === 92 === This research addresses the issue of the incomplete batch arising from estimating the quality of the sample mean from a stochastic simulation experiment by batching method. Batching method is a conceptually straightforward approach, which divides observations in groups in a way that each batch contains the correlation structure of an output process. Incomplete batch is a practical situation in which number of observations in the last batch is different from those in previous batches. However, researches in batching method often ignore the existence of incomplete batch for analytic simplicity. Our study revises the assumption that the incomplete batch does not exist. We construct two new estimators of variance of the sample mean: one discarding the observations contained in the incomplete batch and the other considering all of the observation contained in that with a proper weight. We use large sample theory to derive distributions of new variance estimator. We show that both of our new estimators are asymptotically unbiased and the variance of the estimator considering observations contained in the incomplete batch is smaller than that of the estimator discarding observations contained in the incomplete batch. Moreover, we study analytically to quantify the effect of incomplete batch for confidence-interval estimation by using these two estimators.