Add to ORKGOn-line analysis of output data from discrete event stochastic simulation focuses almost entirely on estimation of means. Most “variance estimation” research in simulation refers to the estimation of the variance of the mean, to construct confidence intervals for mean values. There has been little research on the estimation of variance in simulation. We investigate three methods for point and interval estimates of variance and discuss an implementation of the best technique in an extended version of Akaroa2, a quantitative stochastic simulation controller
In stochastic systems, quantiles indicate the level of system performance that can be delivered with...
[[abstract]]The estimation of the variance of point estimators is a classical problem of stochastic ...
Discrete event simulation is well known to be a powerful approach to investigate behaviour of comple...
For sequential output data analysis in non-terminating discrete-event simulation, we consider three ...
Most of steady state simulation outputs are characterized by some degree of dependency between succe...
Sequential analysis of output data during stochastic discrete-event simulation is a very effective p...
Variance reduction techniques are designed to improve the efficiency of stochastic simulations--that...
TR-COSC 03/08Today, many studies of communication networks rely on simulation conducted to assess th...
The credibility of the final results from stochastic simulation has had limited discussion in the si...
With the increase in computing power and software engineering in the past years computer based stoch...
Sequential analysis of simulation output is generally accepted as the most efficient way for securi...
The credibility of estimated confidence intervals for mean values produced by quantitative stochasti...
[[abstract]]© 2010 Elsevier - Estimating the variance of the sample mean from a stochastic process i...
Simulation output analysis involves two major problems: point estimation and standard-error estimati...
We present a new method for obtaining confidence intervals in steady-state simulation. In our replic...
In stochastic systems, quantiles indicate the level of system performance that can be delivered with...
[[abstract]]The estimation of the variance of point estimators is a classical problem of stochastic ...
Discrete event simulation is well known to be a powerful approach to investigate behaviour of comple...
For sequential output data analysis in non-terminating discrete-event simulation, we consider three ...
Most of steady state simulation outputs are characterized by some degree of dependency between succe...
Sequential analysis of output data during stochastic discrete-event simulation is a very effective p...
Variance reduction techniques are designed to improve the efficiency of stochastic simulations--that...
TR-COSC 03/08Today, many studies of communication networks rely on simulation conducted to assess th...
The credibility of the final results from stochastic simulation has had limited discussion in the si...
With the increase in computing power and software engineering in the past years computer based stoch...
Sequential analysis of simulation output is generally accepted as the most efficient way for securi...
The credibility of estimated confidence intervals for mean values produced by quantitative stochasti...
[[abstract]]© 2010 Elsevier - Estimating the variance of the sample mean from a stochastic process i...
Simulation output analysis involves two major problems: point estimation and standard-error estimati...
We present a new method for obtaining confidence intervals in steady-state simulation. In our replic...
In stochastic systems, quantiles indicate the level of system performance that can be delivered with...
[[abstract]]The estimation of the variance of point estimators is a classical problem of stochastic ...
Discrete event simulation is well known to be a powerful approach to investigate behaviour of comple...