A note on bias and mean squared error in steady-state quantile estimation

DOI10.1016/j.orl.2015.05.003zbMath1408.62059OpenAlexW306184754WikidataQ61039779 ScholiaQ61039779MaRDI QIDQ1785384

David F. Muñoz, Adán Ramírez-López

Publication date: 28 September 2018

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

Full work available at URL: https://doi.org/10.1016/j.orl.2015.05.003

zbMATH Keywords

quantile estimationsteady-state simulationbatch meanssimulation output analysis

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Nonparametric estimation (62G05)

Related Items (2)

Sequest: A Sequential Procedure for Estimating Quantiles in Steady-State Simulations ⋮ Performance evaluation of output analysis methods in steady-state simulations

Cites Work

• On the Bahadur representation of sample quantiles for mixing processes
• On the validity of the batch quantile method for Markov chains
• The Bahadur representation of sample quantiles for sequences of strongly mixing random variables
• The Bahadur representation for sample quantiles under weak dependence
• On the Bahadur representation of sample quantiles for dependent sequences
• Markov Chains and Stochastic Stability
• Strong Consistency of the Variance Estimator in Steady-State Simulation Output Analysis
• A Batch Means Methodology for Estimation of a Nonlinear Function of a Steady-State Mean
• A Note on Quantiles in Large Samples

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