Add to ORKGWe describe a practical recipe for determining when to stop a simulation which is intended to estimate quantiles of the unknown distribution of a real-valued random variable. The ability to reliably estimate quantiles translates into an ability to estimate cumulative distribution functions. The description focusses on the practical implementation of the method, and in particular on stopping criteria. Both authors have found the method useful in their day-to-day work
This paper contains a complete procedure for calculating the value of a conditional quantile estimat...
In stochastic systems, quantiles indicate the level of system performance that can be delivered with...
In simulation studies the computer time can be much reduced by using censoring. Here a simple method...
We describe a practical recipe for determining when to stop a simulation which is intended to estima...
We introduce an inference method based on quantiles matching, which is useful for situations where t...
International audienceWe consider the problem of estimating the p-quantile of a distribution when ob...
Simulation results are often limited to mean values, even though this provides very limited informat...
Simulation output data analysis in performance evaluation studies of complex stochastic systems such...
Quantiles are convenient measures of the entire range of values of simulation outputs. However, unli...
In uncertainty quantification of a numerical simulation model output, the classical approach for qua...
Simulation results are often limited to mean values, even though this provides very limited infor- m...
Discrete event simulation is well known to be a powerful approach to investigate behaviour of comple...
Quantiles, which are also known as values-at-risk in finance, frequently arise in practice as measur...
If the distribution of random variable is uknown, we are not able to figure out the value of theoret...
Confidence intervals for the median of estimators or other quantiles were proposed as a substitute f...
This paper contains a complete procedure for calculating the value of a conditional quantile estimat...
In stochastic systems, quantiles indicate the level of system performance that can be delivered with...
In simulation studies the computer time can be much reduced by using censoring. Here a simple method...
We describe a practical recipe for determining when to stop a simulation which is intended to estima...
We introduce an inference method based on quantiles matching, which is useful for situations where t...
International audienceWe consider the problem of estimating the p-quantile of a distribution when ob...
Simulation results are often limited to mean values, even though this provides very limited informat...
Simulation output data analysis in performance evaluation studies of complex stochastic systems such...
Quantiles are convenient measures of the entire range of values of simulation outputs. However, unli...
In uncertainty quantification of a numerical simulation model output, the classical approach for qua...
Simulation results are often limited to mean values, even though this provides very limited infor- m...
Discrete event simulation is well known to be a powerful approach to investigate behaviour of comple...
Quantiles, which are also known as values-at-risk in finance, frequently arise in practice as measur...
If the distribution of random variable is uknown, we are not able to figure out the value of theoret...
Confidence intervals for the median of estimators or other quantiles were proposed as a substitute f...
This paper contains a complete procedure for calculating the value of a conditional quantile estimat...
In stochastic systems, quantiles indicate the level of system performance that can be delivered with...
In simulation studies the computer time can be much reduced by using censoring. Here a simple method...