We consider estimation of a quantile from a discrete distribution. This gives rise to three new ideas, the confidence set for such a quantile, the notion that the associated confidence level can be increased after the data are collected, and that it is legitimate to strive to obtain a singleton confidence set. We develop properties of the sample quantile noting that the behavior for discrete populations is very different from the behavior for continuous populations. We illustrate the results with simulations and examples
Galton used quantiles more than a hundred years ago in describing data. Tukey and Parzen used them i...
Quantile and quantile effect (QE) functions are important tools for descriptive and causal analysis ...
In this thesis, various construction methods for simultaneous confidence intervals for quantiles are...
We consider estimation of a quantile from a discrete distribution. This gives rise to three new idea...
Sample quantiles are consistent estimators for the true quantile and satisfy central limit theorems ...
If the distribution of random variable is uknown, we are not able to figure out the value of theoret...
Quantiles, which are also known as values-at-risk in finance, frequently arise in practice as measur...
The goal of our research is to estimate the quantiles of a distribution from a large set of samples ...
Sample quantiles for discrete distributions The classical definition of sample quantiles and their a...
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...
An alternative (to profile likelihood techniques) to derive confidence intervals is to use the delta...
Quantile regression is a class of methods voted to the modelling of conditional quantiles. In a Bay...
Population quantiles and their functions are important parameters in many applications. For example,...
International audienceThis work studies the estimation of many statistical quantiles under different...
Galton used quantiles more than a hundred years ago in describing data. Tukey and Parzen used them i...
Quantile and quantile effect (QE) functions are important tools for descriptive and causal analysis ...
In this thesis, various construction methods for simultaneous confidence intervals for quantiles are...
We consider estimation of a quantile from a discrete distribution. This gives rise to three new idea...
Sample quantiles are consistent estimators for the true quantile and satisfy central limit theorems ...
If the distribution of random variable is uknown, we are not able to figure out the value of theoret...
Quantiles, which are also known as values-at-risk in finance, frequently arise in practice as measur...
The goal of our research is to estimate the quantiles of a distribution from a large set of samples ...
Sample quantiles for discrete distributions The classical definition of sample quantiles and their a...
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...
An alternative (to profile likelihood techniques) to derive confidence intervals is to use the delta...
Quantile regression is a class of methods voted to the modelling of conditional quantiles. In a Bay...
Population quantiles and their functions are important parameters in many applications. For example,...
International audienceThis work studies the estimation of many statistical quantiles under different...
Galton used quantiles more than a hundred years ago in describing data. Tukey and Parzen used them i...
Quantile and quantile effect (QE) functions are important tools for descriptive and causal analysis ...
In this thesis, various construction methods for simultaneous confidence intervals for quantiles are...