Add to ORKGWe study the sampling properties of two alternative approaches to estimating the conditional distribution of a continuous outcome Y given a vector X of regressors. One approach \u2013 distribution regression \u2013 is based on direct estimation of the conditional distribution function; the other approach \u2013 quantile regression \u2013 is instead based on direct estimation of the conditional quantile function. Indirect estimates of the conditional quantile function and the conditional distribution function may then be obtained by inverting the direct estimates obtained from either approach or, to guarantee monotonicity, their rearranged versions. We provide a systematic comparison of the asymptotic and finite sample performance of monoton...
For fixed α Ε 0, 1., the quantile regression function gives the α th quantile θ αx. in the condition...
We propose tests for structural change in conditional distributions via quantile regressions. To avo...
The classical theory of linear models focuses on the conditional mean function, i.e. the function th...
We study the sampling properties of two alternative approaches to estimating the conditional distrib...
Given a scalar random variable Y and a random vector X defined on the same probability space, the co...
Koenker & Basset, 1978 introduce the quantile regression estimator, that allows to have a more compl...
Quantile regression as introduced by Koenker and Bassett seeks to extend ideas of quantiles to the e...
We propose a notion of conditional vector quantile function and a vector quantile regression.A condi...
Abstract. We propose a notion of conditional vector quantile function and a vector quantile regressi...
Modelling the quantiles of a random variable is facilitated by their equivariance to monotone transf...
Abstract. Quantile regression, as introduced by Koenker and Bassett (1978), may be viewed as an exte...
Allowing for misspecification in the linear conditional quantile function, this paper provides a new...
To date the literature on quantile regression and least absolute deviation regression has assumed ei...
ABSTRACT. We propose an alternative (‘dual regression’) to the quantile regression pro-cess for the ...
Two popular nonparametric conditional quantile estimation methods, local constant fitting and local ...
For fixed α Ε 0, 1., the quantile regression function gives the α th quantile θ αx. in the condition...
We propose tests for structural change in conditional distributions via quantile regressions. To avo...
The classical theory of linear models focuses on the conditional mean function, i.e. the function th...
We study the sampling properties of two alternative approaches to estimating the conditional distrib...
Given a scalar random variable Y and a random vector X defined on the same probability space, the co...
Koenker & Basset, 1978 introduce the quantile regression estimator, that allows to have a more compl...
Quantile regression as introduced by Koenker and Bassett seeks to extend ideas of quantiles to the e...
We propose a notion of conditional vector quantile function and a vector quantile regression.A condi...
Abstract. We propose a notion of conditional vector quantile function and a vector quantile regressi...
Modelling the quantiles of a random variable is facilitated by their equivariance to monotone transf...
Abstract. Quantile regression, as introduced by Koenker and Bassett (1978), may be viewed as an exte...
Allowing for misspecification in the linear conditional quantile function, this paper provides a new...
To date the literature on quantile regression and least absolute deviation regression has assumed ei...
ABSTRACT. We propose an alternative (‘dual regression’) to the quantile regression pro-cess for the ...
Two popular nonparametric conditional quantile estimation methods, local constant fitting and local ...
For fixed α Ε 0, 1., the quantile regression function gives the α th quantile θ αx. in the condition...
We propose tests for structural change in conditional distributions via quantile regressions. To avo...
The classical theory of linear models focuses on the conditional mean function, i.e. the function th...