Confidence intervals for the median of estimators or other quantiles were proposed as a substitute for usual confidence intervals in terminating and steady-state simulation. This is adequate since for many estimators the median and the expectation are close together or coincide, particularly if the sample size is large. The novel confidence intervals are easy to obtain, the variance of the estimator is not used. They are well suited for correlated simulation output data, apply to functions of estimators, and in simulation they seem to be particularly accurate. For the estimation of quantiles by order statistics, the new confidence intervals are exact.
Sequential analysis of simulation output is generally accepted as the most efficient way for securi...
This article is concerned with the calculation of confidence intervals for simulation output that is...
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...
Confidence intervals provide a way of reporting an estimate of a population quantile along with some...
In this thesis, various construction methods for simultaneous confidence intervals for quantiles are...
Quantiles, which are also known as values-at-risk in finance, frequently arise in practice as measur...
We describe and compare methods for constructing confidence intervals for quantile regression coeffi...
A method for estimating quantiles and their confidence intervals based on the paper by Francisco-Ful...
We propose a new empirical likelihood approach which can be used to construct non-parametric (design...
In the paper selected nonparametric and semiparametric estimation methods of higher orders quantiles...
This paper presents a new random weighting method for confidence interval estimation for the sample ...
Quantiles and percentiles represent useful statistical tools for describing the distribution of resu...
Most of steady state simulation outputs are characterized by some degree of dependency between succe...
Schruben (1983) developed standardized time series (STS) methods to construct confidence intervals (...
Sequential analysis of simulation output is generally accepted as the most efficient way for securi...
This article is concerned with the calculation of confidence intervals for simulation output that is...
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...
Confidence intervals provide a way of reporting an estimate of a population quantile along with some...
In this thesis, various construction methods for simultaneous confidence intervals for quantiles are...
Quantiles, which are also known as values-at-risk in finance, frequently arise in practice as measur...
We describe and compare methods for constructing confidence intervals for quantile regression coeffi...
A method for estimating quantiles and their confidence intervals based on the paper by Francisco-Ful...
We propose a new empirical likelihood approach which can be used to construct non-parametric (design...
In the paper selected nonparametric and semiparametric estimation methods of higher orders quantiles...
This paper presents a new random weighting method for confidence interval estimation for the sample ...
Quantiles and percentiles represent useful statistical tools for describing the distribution of resu...
Most of steady state simulation outputs are characterized by some degree of dependency between succe...
Schruben (1983) developed standardized time series (STS) methods to construct confidence intervals (...
Sequential analysis of simulation output is generally accepted as the most efficient way for securi...
This article is concerned with the calculation of confidence intervals for simulation output that is...
If the distribution of random variable is uknown, we are not able to figure out the value of theoret...