Autoregressive-output-analysis methods revisited

DOI10.1007/BF02136836zbMath0817.62075MaRDI QIDQ1805485

Mingjian Yuan, Barry L. Nelson

Publication date: 3 July 1995

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

zbMATH Keywords

time seriesconfidence intervalstrong consistencydegrees of freedomorder estimatorasymptotically valid confidence-interval procedureautoregressive orderautoregressive-output-analysis methodpredictive least-squares criterionsteady-state mean of a simulated process

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Parametric tolerance and confidence regions (62F25) Statistical tables (62Q05) Probabilistic methods, stochastic differential equations (65C99)

Related Items (2)

Confidence intervals for the difference between normal means with known coefficients of variation ⋮ Estimating the steady-state mean from short transient simulations

Cites Work

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• Strong consistency of the PLS criterion for order determination of autoregressive processes
• Arma-Based Confidence Intervals for Simulation Output Analysis
• Order selection for AR models by predictive least squares
• The Bias of Autoregressive Coefficient Estimators
• Maximum likelihood identification of Gaussian autoregressive moving average models
• On a measure of lack of fit in time series models
• The Effect of Serial Correlation on the Performance of CUSUM Tests
• Multiple comparisons with the best for steady-state simulation

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