Name
Abstract | ||
|---|---|---|
Consider a stationary stochastic process, X"1,X"2,..., arising from a steady-state simulation. An important problem is that of estimating the expected value @m of the process. The usual estimator for @m is the sample mean based on n observations, X@?"n, and a measure of the precision of X@?"n is the variance parameter, @s^2=lim"n"-"~nVar[X@?"n]. This paper studies asymptotic properties of the batch-means estimator V@^"B(b,m) for @s^2 as both the batch size m and number of batches b become large. In particular, we give conditions for V@^"B(b,m) to converge to normality as m and b increase. Empirical examples illustrate our findings. |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1016/S0167-6377(02)00156-6 | Oper. Res. Lett. |
Keywords | Field | DocType |
paper studies asymptotic property,b increase,batch size m,steady-state simulation,important problem,stationary stochastic process,expected value,batch-means variance estimator,n observation,usual estimator,empirical example,large-sample normality,simulation,stationary process,steady state,stochastic process | Normality,Mathematical optimization,Sample mean and sample covariance,Variance estimation,Stochastic process,Stationary process,Bias of an estimator,Expected value,Mathematics,Estimator | Journal |
Volume | Issue | ISSN |
30 | 5 | Operations Research Letters |
Citations | PageRank | References |
1 | 0.37 | 5 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michael Sherman | 1 | 15 | 3.81 |
| David Goldsman | 2 | 904 | 159.71 |