Add to ORKGConfidence intervals for the median of estimators or other quantiles were proposed as a substitute for usual confidence intervals in terminating and steady-state simulation. They 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.
Most of steady state simulation outputs are characterized by some degree of dependency between succe...
An alternative (to profile likelihood techniques) to derive confidence intervals is to use the delta...
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...
This paper presents a new random weighting method for confidence interval estimation for the sample ...
In the paper selected nonparametric and semiparametric estimation methods of higher orders quantiles...
We describe a non-parametric method based on the sole assumption that the data points form an i.i.d ...
We propose a new empirical likelihood approach which can be used to construct non-parametric (design...
Schruben (1983) developed standardized time series (STS) methods to construct confidence intervals (...
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...
An alternative (to profile likelihood techniques) to derive confidence intervals is to use the delta...
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...
This paper presents a new random weighting method for confidence interval estimation for the sample ...
In the paper selected nonparametric and semiparametric estimation methods of higher orders quantiles...
We describe a non-parametric method based on the sole assumption that the data points form an i.i.d ...
We propose a new empirical likelihood approach which can be used to construct non-parametric (design...
Schruben (1983) developed standardized time series (STS) methods to construct confidence intervals (...
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...
An alternative (to profile likelihood techniques) to derive confidence intervals is to use the delta...
If the distribution of random variable is uknown, we are not able to figure out the value of theoret...