<p>The 5% (red line), 10% (green line), 25% (dark blue line), 50% (blue), 75% (dark pink line), 90% (grey line) and 95% (pink line) percentiles are shown. When using quantile regression by examining multiple percentiles rather than only the mean, one can examine how the entire distribution of FEF<sub>50%</sub> changes with sRaw<sub>0.5</sub>.</p
Estimating the conditional quantiles of outcome variables of interest is frequent in many research a...
In this paper we develop the nonparametric QR series framework, covering many regressors as a specia...
We study the sampling properties of two alternative approaches to estimating the conditional distrib...
Quantile regression is about estimating the quantiles of some d-dimensional response Y conditional o...
Quantile regression (QR) fits a linear model for conditional quantiles, just as ordinary least squar...
Quantile regression extends ordinary least-squares regression to quantiles of the response variable....
Koenker & Basset, 1978 introduce the quantile regression estimator, that allows to have a more compl...
Abstract. Classical least squares regression may be viewed as a natural way of extending the idea of...
Possibly misspecified linear quantile regression models are considered. A measure for assessing the ...
In ordinary quantile regression, quantiles of different order are estimated one at a time. An altern...
This study aims to compare the different between two data sets that having the relationship between...
<p>Regression coefficient estimates for linear regression of <i>F</i><sub>ST</sub> on 2% quantile bi...
For fixed α Ε 0, 1., the quantile regression function gives the α th quantile θ αx. in the condition...
An introductory section shows the behavior of quantile regressions in datasets with different charac...
From a regression with ranked publications on the left hand side and the same controls as above on t...
Estimating the conditional quantiles of outcome variables of interest is frequent in many research a...
In this paper we develop the nonparametric QR series framework, covering many regressors as a specia...
We study the sampling properties of two alternative approaches to estimating the conditional distrib...
Quantile regression is about estimating the quantiles of some d-dimensional response Y conditional o...
Quantile regression (QR) fits a linear model for conditional quantiles, just as ordinary least squar...
Quantile regression extends ordinary least-squares regression to quantiles of the response variable....
Koenker & Basset, 1978 introduce the quantile regression estimator, that allows to have a more compl...
Abstract. Classical least squares regression may be viewed as a natural way of extending the idea of...
Possibly misspecified linear quantile regression models are considered. A measure for assessing the ...
In ordinary quantile regression, quantiles of different order are estimated one at a time. An altern...
This study aims to compare the different between two data sets that having the relationship between...
<p>Regression coefficient estimates for linear regression of <i>F</i><sub>ST</sub> on 2% quantile bi...
For fixed α Ε 0, 1., the quantile regression function gives the α th quantile θ αx. in the condition...
An introductory section shows the behavior of quantile regressions in datasets with different charac...
From a regression with ranked publications on the left hand side and the same controls as above on t...
Estimating the conditional quantiles of outcome variables of interest is frequent in many research a...
In this paper we develop the nonparametric QR series framework, covering many regressors as a specia...
We study the sampling properties of two alternative approaches to estimating the conditional distrib...