Cramer-von Mises variance estimators for simulations
成果类型：
Article
署名作者：
Goldsman, D; Kang, KB; Seila, AF
署名单位：
University System of Georgia; Georgia Institute of Technology; United States Department of Defense; United States Navy; Naval Postgraduate School; University System of Georgia; University of Georgia
刊物名称：
OPERATIONS RESEARCH
ISSN/ISSBN：
0030-364X
DOI：
10.1287/opre.47.2.299
发表日期：
1999
页码：
299-309
关键词：
摘要：
We study estimators for the variance parameter sigma(2) of a stationary process. The estimators are based on weighted Cramer-von Mises statistics, and certain weightings yield estimators that are first-order unbiased for sigma(2). We derive an expression for the asymptotic variance of the new estimators; this expression is then used to obtain the first-order unbiased estimator having the smallest variance among fu;ed-degree polynomial weighting functions. Our work is based on asymptotic theory; however, we present exact and empirical examples to demonstrate the new estimators' small-sample robustness. We use a single batch of observations to derive the estimators' asymptotic properties, and then we compare the new estimators among one another. In real-life applications, one would use more than one batch; we indicate how this generalization can be carried out.