Area variance estimators for simulation using folded standardized time series

Claudia Antonini, Christos Alexopoulos, David Goldsman, James Wilson

Producción científica: Contribución a una revistaArtículorevisión exhaustiva

5 Citas (Scopus)

Resumen

We estimate the variance parameter of a stationary simulation-generated process using "folded" versions of standardized time series area estimators. Asymptotically as the sample size increases, different folding levels yield unbiased estimators that are independent scaled chi-squared variates, each with one degree of freedom. This result is exploited to formulate improved variance estimators based on the combination of multiple levels as well as the use of batching. The improved estimators preserve the asymptotic bias properties of their predecessors, but have substantially lower asymptotic variances. The performance of the new variance estimators is demonstrated in a first-order autoregressive process with autoregressive parameter 0.9 and in the queue-waiting-time process for an M/M/1 queue with server utilization 0.8.

Idioma originalInglés
Páginas (desde-hasta)134-144
Número de páginas11
PublicaciónIIE Transactions (Institute of Industrial Engineers)
Volumen41
N.º2
DOI
EstadoPublicada - 2009
Publicado de forma externa

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