In this paper, we investigate the asymptotic properties of a nonparametric conditional quantile estimation in the single functional index model for dependent functional data and censored at random responses are observed. First of all, we establish asymptotic properties for a conditional distribution estimator from which we derive an central limit theorem (CLT) of the conditional quantile estimator. Simulation study is also presented to illustrate the validity and finite sample performance of the considered estimator. Finally, the estimation of the functional index via the pseudo-maximum likelihood method is discussed, but not tackled
This paper makes two main contributions to inference for conditional quantiles. First, we construct ...
This paper deals with the conditional hazard estimator of a real response where the variable is give...
When the dimension of the covariate space is high, semiparametric regression models become indispens...
The main objective of this paper is to non-parametrically estimate the quantiles of a conditional di...
The main objective of this paper is to estimate non-parametrically the quantiles of a conditional d...
The aim of this paper is to estimate nonparametrically the conditional quantile density function. A ...
We consider the problem of nonparametrically estimating the conditional quantile function from censo...
In this thesis we study some asymptotic properties of the kernel conditional quantile estimator when...
The main objective of this paper is to investigate the nonparametric estimation of the conditional d...
This paper proposes a fully nonparametric procedure for testing conditional quantile independence. T...
AbstractWe address the estimation of quantiles from heavy-tailed distributions when functional covar...
When the dimension of the covariate space is high, semiparametric regression models become indispens...
We consider nonparametric estimation of the conditional qth quantile for stationary time series. We ...
Abstract − We address the estimation of quantiles from heavy-tailed dis-tributions when functional c...
This paper develops a nonparametric method to estimate a conditional quantile function for a panel d...
This paper makes two main contributions to inference for conditional quantiles. First, we construct ...
This paper deals with the conditional hazard estimator of a real response where the variable is give...
When the dimension of the covariate space is high, semiparametric regression models become indispens...
The main objective of this paper is to non-parametrically estimate the quantiles of a conditional di...
The main objective of this paper is to estimate non-parametrically the quantiles of a conditional d...
The aim of this paper is to estimate nonparametrically the conditional quantile density function. A ...
We consider the problem of nonparametrically estimating the conditional quantile function from censo...
In this thesis we study some asymptotic properties of the kernel conditional quantile estimator when...
The main objective of this paper is to investigate the nonparametric estimation of the conditional d...
This paper proposes a fully nonparametric procedure for testing conditional quantile independence. T...
AbstractWe address the estimation of quantiles from heavy-tailed distributions when functional covar...
When the dimension of the covariate space is high, semiparametric regression models become indispens...
We consider nonparametric estimation of the conditional qth quantile for stationary time series. We ...
Abstract − We address the estimation of quantiles from heavy-tailed dis-tributions when functional c...
This paper develops a nonparametric method to estimate a conditional quantile function for a panel d...
This paper makes two main contributions to inference for conditional quantiles. First, we construct ...
This paper deals with the conditional hazard estimator of a real response where the variable is give...
When the dimension of the covariate space is high, semiparametric regression models become indispens...