Improved point and confidence interval estimators of mean response in simulation when control variates are used

DOI10.1080/03610919308813150zbMath0800.62362OpenAlexW2018198401MaRDI QIDQ4275715

Ming Tan, Leon Jay Gleser

Publication date: 20 January 1994

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610919308813150

Mathematics Subject Classification ID

Parametric tolerance and confidence regions (62F25) Linear regression; mixed models (62J05) Probabilistic methods, stochastic differential equations (65C99)

Related Items (3)

Unbiased estimation of the common mean of a multivariate normal distribution ⋮ Confidence intervals for means and variances of nonnormal distributions ⋮ On combining correlated estimators of the common mean of a multivariate normal distribution

Cites Work

• Improved confidence sets for the coefficients of a linear model with spherically symmetric errors
• A family of minimax estimators in some multiple regression problems
• Minimax confidence sets for the mean of a multivariate normal distribution
• Variance reduction in queueing simulation using generalized concomitant variables
• Efficiency of Multivariate Control Variates in Monte Carlo Simulation
• A perspective on variance reduction in dynamic simulation experiments
• Monte Carlo, Control Variates, and Stochastic Ordering
• Improved set estimators for the coefficients of a linear model when the error distribution is spherically symmetric with unknown variances
• A Perspective on the Use of Control Variables to Increase the Efficiency of Monte Carlo Simulations
• Statistical Results on Control Variables with Application to Queueing Network Simulation
• Estimation of Multiresponse Simulation Metamodels Using Control Variates
• Non-Optimality of Preliminary-Test Estimators for the Mean of a Multivariate Normal Distribution

This page was built for publication: Improved point and confidence interval estimators of mean response in simulation when control variates are used