Mean-Square Consistency of the Variance Estimator in Steady-State Simulation Output Analysis

DOI10.1287/opre.43.2.282zbMath0830.62077OpenAlexW2138011412MaRDI QIDQ4849328

Halim Damerdji

Publication date: 25 September 1995

Published in: Operations Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1287/opre.43.2.282

zbMATH Keywords

biascentral limit theoremspartial-sum processbatch meansregenerative processesstrong approximationsstrong invariance principlestandardized time seriesmean-square consistencyspaced batch meansoverlapping batch meanssteady-state simulation output analysisarea variance estimatorshigher centered momentsprocess-variance estimator

Mathematics Subject Classification ID

Non-Markovian processes: estimation (62M09) Strong limit theorems (60F15) Inference from stochastic processes (62M99) Probabilistic methods, stochastic differential equations (65C99)

Related Items

Markov chain Monte Carlo confidence intervals ⋮ An improved standardized time series Durbin-Watson variance estimator for steady-state simulation ⋮ New recursive estimators of the time-average variance constant ⋮ Globally Centered Autocovariances in MCMC ⋮ Batch means and spectral variance estimators in Markov chain Monte Carlo ⋮ Folded overlapping variance estimators for simulation ⋮ Batch size selection for variance estimators in MCMC ⋮ Strong invariance principles for ergodic Markov processes ⋮ Kernel estimators of asymptotic variance for adaptive Markov chain Monte Carlo ⋮ Combining standardized time series area and Cramér–von Mises variance estimators ⋮ Data-Based Choice of Batch Size for Simulation Output Analysis ⋮ Batch variance estimators for the median of simulation output. ⋮ Large-sample normality of the batch-means variance estimator ⋮ Overlapping Batches for the Assessment of Solution Quality in Stochastic Programs