Quantile regression extends ordinary least-squares regression to quantiles of the response variable. Ordinary regression is a model for the conditional mean, where the mean is conditional on the value of the explanatory variable. Likewise, quantile regression is a model for the conditional quantiles. For trend analysis the explanatory variable is time. Quantiles are points taken at regular intervals from the cumulative distribution function of a random variable. The quantiles mark a set of ordered data into equal-sized data subsets
Koenker & Basset, 1978 introduce the quantile regression estimator, that allows to have a more compl...
Volume two of Quantile Regression offers an important guide for applied researchers that draws on th...
Abstract: Recent interest in modern regressionmodelling has focused on extending available (mean) re...
Abstract. Quantile regression, as introduced by Koenker and Bassett (1978), may be viewed as an exte...
Abstract. Classical least squares regression may be viewed as a natural way of extending the idea of...
Quantile regression offers a more complete statistical model than mean regression and now has widesp...
A guide to the implementation and interpretation of Quantile Regression models. This book explores t...
Quantile regression model has caught a lot of attention lately in many areas including statistics an...
Quantile regression provides a method for estimating quantiles of a distribution while incorporating...
Quantile regression as introduced in Koenker and Bassett (1978) may be viewed as a natural extension...
A guide to the implementation and interpretation of Quantile Regression models This book explores t...
A comprehensive treatment of the subject, encompassing models that are linear and nonlinear, paramet...
For fixed α Ε 0, 1., the quantile regression function gives the α th quantile θ αx. in the condition...
Volume two of Quantile Regression offers an important guide for applied researchers that draws on th...
Abstract. Quantile regression is an evolving body of statistical methods for estimating and drawing ...
Koenker & Basset, 1978 introduce the quantile regression estimator, that allows to have a more compl...
Volume two of Quantile Regression offers an important guide for applied researchers that draws on th...
Abstract: Recent interest in modern regressionmodelling has focused on extending available (mean) re...
Abstract. Quantile regression, as introduced by Koenker and Bassett (1978), may be viewed as an exte...
Abstract. Classical least squares regression may be viewed as a natural way of extending the idea of...
Quantile regression offers a more complete statistical model than mean regression and now has widesp...
A guide to the implementation and interpretation of Quantile Regression models. This book explores t...
Quantile regression model has caught a lot of attention lately in many areas including statistics an...
Quantile regression provides a method for estimating quantiles of a distribution while incorporating...
Quantile regression as introduced in Koenker and Bassett (1978) may be viewed as a natural extension...
A guide to the implementation and interpretation of Quantile Regression models This book explores t...
A comprehensive treatment of the subject, encompassing models that are linear and nonlinear, paramet...
For fixed α Ε 0, 1., the quantile regression function gives the α th quantile θ αx. in the condition...
Volume two of Quantile Regression offers an important guide for applied researchers that draws on th...
Abstract. Quantile regression is an evolving body of statistical methods for estimating and drawing ...
Koenker & Basset, 1978 introduce the quantile regression estimator, that allows to have a more compl...
Volume two of Quantile Regression offers an important guide for applied researchers that draws on th...
Abstract: Recent interest in modern regressionmodelling has focused on extending available (mean) re...