Add to ORKGQuantile regression provides a method for estimating quantiles of a distribution while incorporating covariate information. If distributional assumptions are placed on the distribution of the response variable, an asymptotic variance estimate for the estimators can be obtained. This is done for a variety of distributions and comparisons are made to equivalent simple linear regression models
For fixed α Ε 0, 1., the quantile regression function gives the α th quantile θ αx. in the condition...
Quantile regression is a class of methods voted to the modelling of conditional quantiles. In a Bay...
A comprehensive treatment of the subject, encompassing models that are linear and nonlinear, paramet...
Quantile regression provides a method for estimating quantiles of a distribution while incorporating...
Quantile regression, as introduced by Koenker and Bassett (1978), may be viewed as an extension of c...
Quantile regression offers a more complete statistical model than mean regression and now has widesp...
Quantile regression extends ordinary least-squares regression to quantiles of the response variable....
A guide to the implementation and interpretation of Quantile Regression models. This book explores t...
A guide to the implementation and interpretation of Quantile Regression models This book explores t...
Abstract. Quantile regression, as introduced by Koenker and Bassett (1978), may be viewed as an exte...
Quantile regression model has caught a lot of attention lately in many areas including statistics an...
We consider the problem of estimating quantile regression coefficients in errorsin -variables models...
To date the literature on quantile regression and least absolute deviation regression has assumed ei...
Possibly misspecified linear quantile regression models are considered. A measure for assessing the ...
This paper establishes that the availability of instrumental variables enables the identification an...
For fixed α Ε 0, 1., the quantile regression function gives the α th quantile θ αx. in the condition...
Quantile regression is a class of methods voted to the modelling of conditional quantiles. In a Bay...
A comprehensive treatment of the subject, encompassing models that are linear and nonlinear, paramet...
Quantile regression provides a method for estimating quantiles of a distribution while incorporating...
Quantile regression, as introduced by Koenker and Bassett (1978), may be viewed as an extension of c...
Quantile regression offers a more complete statistical model than mean regression and now has widesp...
Quantile regression extends ordinary least-squares regression to quantiles of the response variable....
A guide to the implementation and interpretation of Quantile Regression models. This book explores t...
A guide to the implementation and interpretation of Quantile Regression models This book explores t...
Abstract. Quantile regression, as introduced by Koenker and Bassett (1978), may be viewed as an exte...
Quantile regression model has caught a lot of attention lately in many areas including statistics an...
We consider the problem of estimating quantile regression coefficients in errorsin -variables models...
To date the literature on quantile regression and least absolute deviation regression has assumed ei...
Possibly misspecified linear quantile regression models are considered. A measure for assessing the ...
This paper establishes that the availability of instrumental variables enables the identification an...
For fixed α Ε 0, 1., the quantile regression function gives the α th quantile θ αx. in the condition...
Quantile regression is a class of methods voted to the modelling of conditional quantiles. In a Bay...
A comprehensive treatment of the subject, encompassing models that are linear and nonlinear, paramet...