Add to ORKGLet $ {(X_i, Y_i) : i = 1, 2, ldots } $ be a sequence of stationary independent random vectors in $ Re^{(2)} $ with a continuous distribution, and let $ G_x(cdot) $ denote the conditional distribution function of $ Y_1 $ given $ X_1 = x $. In this paper, Bahadur\u27s almost sure representation for the sample conditional quantile $ hat{G}_{nx}^{-1} $, $ 0 < lambda <1 $, is established, where $ hat{G}_{nx} $, is a smoothed (rank nearest neighbor or the Nadaraya-Watson type) estimator of $ hat{G}_{nx} $. Such representations arc useful in the study of asymptotics of functionals of conditional quantiles
In this paper a new nonparametric estimate of conditional quantiles is proposed, that avoids the pro...
Abstract. We propose a notion of conditional vector quantile function and a vector quantile regressi...
In this paper we derive the asymptotic distribution of a new class of quasi-maximum likelihood estim...
Let {(X1, Y1) : i = 1, 2,...; be a sequence of stationary independent random vectors in:71(2) with a...
Bahadur-type representations of nearest-neighbor and kernel estimators of the conditional ＄P＄-quanti...
Let $ {(X_i, Y_i) : i = 1, 2, ldots } $ be a sequence of independent identically distributed random ...
Let (X, Y) be a random vector such that X is d-dimensional, Y is real valued, and θ(X)is the co...
AbstractLet (X, Y) be a random vector such that X is d-dimensional, Y is real valued, and θ(X) is th...
Suppose that we observe bivariate data (Xi, Yi) only when Yi ≤ Xi (left truncation). Denote with F t...
The main objective of this paper is to estimate non-parametrically the quantiles of a conditional d...
Let (X, Y) be a two dimensional random variable with a joint density function f(x, y) and a joint di...
AbstractU-quantiles are applied in robust statistics, like the Hodges–Lehmann estimator of location ...
Charlier, Paindaveine, and Saracco (2014) recently introduced a nonparametric estimatorof conditiona...
We propose a notion of conditional vector quantile function and a vector quantile regression.A condi...
In this paper, we construct a new class of estimators for conditional quantiles in possibly misspec...
In this paper a new nonparametric estimate of conditional quantiles is proposed, that avoids the pro...
Abstract. We propose a notion of conditional vector quantile function and a vector quantile regressi...
In this paper we derive the asymptotic distribution of a new class of quasi-maximum likelihood estim...
Let {(X1, Y1) : i = 1, 2,...; be a sequence of stationary independent random vectors in:71(2) with a...
Bahadur-type representations of nearest-neighbor and kernel estimators of the conditional ＄P＄-quanti...
Let $ {(X_i, Y_i) : i = 1, 2, ldots } $ be a sequence of independent identically distributed random ...
Let (X, Y) be a random vector such that X is d-dimensional, Y is real valued, and θ(X)is the co...
AbstractLet (X, Y) be a random vector such that X is d-dimensional, Y is real valued, and θ(X) is th...
Suppose that we observe bivariate data (Xi, Yi) only when Yi ≤ Xi (left truncation). Denote with F t...
The main objective of this paper is to estimate non-parametrically the quantiles of a conditional d...
Let (X, Y) be a two dimensional random variable with a joint density function f(x, y) and a joint di...
AbstractU-quantiles are applied in robust statistics, like the Hodges–Lehmann estimator of location ...
Charlier, Paindaveine, and Saracco (2014) recently introduced a nonparametric estimatorof conditiona...
We propose a notion of conditional vector quantile function and a vector quantile regression.A condi...
In this paper, we construct a new class of estimators for conditional quantiles in possibly misspec...
In this paper a new nonparametric estimate of conditional quantiles is proposed, that avoids the pro...
Abstract. We propose a notion of conditional vector quantile function and a vector quantile regressi...
In this paper we derive the asymptotic distribution of a new class of quasi-maximum likelihood estim...