Title
Standardized time series methods: variance estimation using replicated batch means
Abstract
We present a new method for obtaining confidence intervals in steady-state simulation. In our replicated batch means method, we do a small number of independent replications to estimate the steady-state mean of the underlying stochastic process. In order to obtain a variance estimator, we further group the observations from these replications into non-overlapping batches. We show that for large sample sizes, the new variance estimator is less biased than the batch means variance estimator, the variances of the two variance estimators are approximately equal, and the new steady-state mean estimator has a smaller variance than the batch means estimator when there is positive serial correlation between the observations. For small sample sizes, we compare our replicated batch means method with the (standard) batch means and multiple replications methods empirically, and show that the best overall coverage of confidence intervals is obtained by the replicated batch means method with a small number of replications.
Year
DOI
Venue
2001
10.1145/564124.564172
Winter Simulation Conference
Keywords
Field
DocType
non-overlapping batch,new steady-state mean estimator,variance estimation,confidence interval,new method,smaller variance,standardized time series method,multiple replications methods empirically,batch mean,small number,variance estimator,new variance estimator,stochastic process,time series,sample size,serial correlation,steady state
Small number,One-way analysis of variance,Mean squared error,Bias of an estimator,Statistics,Confidence interval,Sample size determination,Mathematics,Estimator,Autocorrelation
Conference
ISBN
Citations 
PageRank 
0-7803-7309-X
1
0.41
References 
Authors
2
2
Name
Order
Citations
PageRank
Sigrún Andradóttir154855.09
Nilay Tanik Argon2404.22