Add to ORKG<正> The ordinary quantiles for univariate data were successfully generalized to linear modelsin Koenker and Bassett. Regression quantiles provide more specific and more global in-formation on the relationship of two variables through their distributions. Mosteller andTukey argued that the use of reg...SCI(E)08627-6314
Résumé. Nous construisons un estimateur non-paramétrique des quantiles conditionnels de Y sachant X ...
In regression, the desired estimate of y|x is not always given by a conditional mean, although this...
Small-sample properties of a nonparametric estimator of conditional quantiles based on optimal quant...
Quantile regression as introduced by Koenker and Bassett seeks to extend ideas of quantiles to the e...
International audienceWe construct a nonparametric estimator of conditional quantiles of Y given X =...
This paper makes two main contributions to inference for conditional quantiles. First, we construct ...
Let (X, Y) be a random vector such that X is d-dimensional, Y is real valued, and [theta](X) is the ...
Allowing for misspecification in the linear conditional quantile function, this paper provides a new...
Abstract. Quantile regression, as introduced by Koenker and Bassett (1978), may be viewed as an exte...
This paper makes two main contributions to inference for conditional quantiles. First, we construct ...
AbstractLet (X, Y) be a random vector such that X is d-dimensional, Y is real valued, and θ(X) is th...
For fixed α Ε 0, 1., the quantile regression function gives the α th quantile θ αx. in the condition...
One of the most common applications of nonparametric techniques has been the estimation of a regress...
This paper proposes a fully nonparametric procedure for testing conditional quantile independence. T...
Charlier, Paindaveine, and Saracco (2014) recently introduced a nonparametric estimatorof conditiona...
Résumé. Nous construisons un estimateur non-paramétrique des quantiles conditionnels de Y sachant X ...
In regression, the desired estimate of y|x is not always given by a conditional mean, although this...
Small-sample properties of a nonparametric estimator of conditional quantiles based on optimal quant...
Quantile regression as introduced by Koenker and Bassett seeks to extend ideas of quantiles to the e...
International audienceWe construct a nonparametric estimator of conditional quantiles of Y given X =...
This paper makes two main contributions to inference for conditional quantiles. First, we construct ...
Let (X, Y) be a random vector such that X is d-dimensional, Y is real valued, and [theta](X) is the ...
Allowing for misspecification in the linear conditional quantile function, this paper provides a new...
Abstract. Quantile regression, as introduced by Koenker and Bassett (1978), may be viewed as an exte...
This paper makes two main contributions to inference for conditional quantiles. First, we construct ...
AbstractLet (X, Y) be a random vector such that X is d-dimensional, Y is real valued, and θ(X) is th...
For fixed α Ε 0, 1., the quantile regression function gives the α th quantile θ αx. in the condition...
One of the most common applications of nonparametric techniques has been the estimation of a regress...
This paper proposes a fully nonparametric procedure for testing conditional quantile independence. T...
Charlier, Paindaveine, and Saracco (2014) recently introduced a nonparametric estimatorof conditiona...
Résumé. Nous construisons un estimateur non-paramétrique des quantiles conditionnels de Y sachant X ...
In regression, the desired estimate of y|x is not always given by a conditional mean, although this...
Small-sample properties of a nonparametric estimator of conditional quantiles based on optimal quant...