Confidence intervals provide a way of reporting an estimate of a population quantile along with some information about the precision of estimates. Some procedures that may be used to obtain estimates of confidence intervals for quantiles in a finite population (most of which are based on resampling) are compared in the paper. A simulation study, based on two different artificial populations, is performed and comparisons of the estimation methods proposed for confidence intervals of population quantiles are made. First Published Online: 14 Oct 201
For any estimate of response, confidence intervals are important as they help quantify a plausible r...
In this thesis, various construction methods for simultaneous confidence intervals for quantiles are...
<p>Quantiles, which are also known as values-at-risk in finance, frequently arise in practice as mea...
Confidence intervals provide a way of reporting an estimate of a population quantile along with some...
The aim of the paper is to study the problem of estimating the quantile function of a finite populat...
A confidence interval is a standard way of expressing our uncertainty about the value of a populatio...
The aim of the paper is to study the problem of estimating the quantile function of a finite populati...
For any estimate of response, confidence intervals are important as they help quantify a plausible r...
Confidence intervals for the median of estimators or other quantiles were proposed as a substitute f...
We propose a new empirical likelihood approach which can be used to construct non-parametric (design...
Beran & Hall's (1993) simple linear interpolation provides a very convenient approach for constructi...
Confidence intervals for the median of estimators or other quantiles were proposed as a substitute f...
Beran & Hall's (1993) simple linear interpolation provides a very convenient approach for constructi...
A method for estimating quantiles and their confidence intervals based on the paper by Francisco-Ful...
We consider estimation of a quantile from a discrete distribution. This gives rise to three new idea...
For any estimate of response, confidence intervals are important as they help quantify a plausible r...
In this thesis, various construction methods for simultaneous confidence intervals for quantiles are...
<p>Quantiles, which are also known as values-at-risk in finance, frequently arise in practice as mea...
Confidence intervals provide a way of reporting an estimate of a population quantile along with some...
The aim of the paper is to study the problem of estimating the quantile function of a finite populat...
A confidence interval is a standard way of expressing our uncertainty about the value of a populatio...
The aim of the paper is to study the problem of estimating the quantile function of a finite populati...
For any estimate of response, confidence intervals are important as they help quantify a plausible r...
Confidence intervals for the median of estimators or other quantiles were proposed as a substitute f...
We propose a new empirical likelihood approach which can be used to construct non-parametric (design...
Beran & Hall's (1993) simple linear interpolation provides a very convenient approach for constructi...
Confidence intervals for the median of estimators or other quantiles were proposed as a substitute f...
Beran & Hall's (1993) simple linear interpolation provides a very convenient approach for constructi...
A method for estimating quantiles and their confidence intervals based on the paper by Francisco-Ful...
We consider estimation of a quantile from a discrete distribution. This gives rise to three new idea...
For any estimate of response, confidence intervals are important as they help quantify a plausible r...
In this thesis, various construction methods for simultaneous confidence intervals for quantiles are...
<p>Quantiles, which are also known as values-at-risk in finance, frequently arise in practice as mea...