When analyzing time-to-event data, it often happens that a certain fraction of the data corresponds to subjects who will never experience the event of interest. These event times are considered as infinite and the subjects are said to be cured. Survival models that take this feature into account...

Suppose the random vector (X,Y) satisfies the regression model Y=m(X)+[sigma](X)[epsilon], where m([dot operator]) is the conditional mean, [sigma]2([dot operator]) is the conditional variance, and [epsilon] is independent of X. The covariate X is d-dimensional (d=1), the response Y is...

In this paper we consider the estimation of the error distribution in a heteroscedastic nonparametric regression model with multivariate covariates. As estimator we consider the empirical distribution function of residuals, which are obtained from multivariate local polynomial fits of the...

Consider the heteroscedastic model Y=m(X)+[sigma](X)[var epsilon], where [var epsilon] and X are independent, Y is subject to right censoring, m(·) is an unknown but smooth location function (like e.g. conditional mean, median, trimmed mean...) and [sigma](·) an unknown but smooth scale...

We provide easy to verify sufficient conditions for the consistency and asymptotic normality of a class of semiparametric optimization estimators where the criterion function does not obey standard smoothness conditions and simultaneously depends on some nonparametric estimators that can...

Possibly misspecified linear quantile regression models are considered. A measure for assessing the combined effect of several covariates on a certain conditional quantile function is proposed. The measure is based on an adaptation to quantile regression of the famous coefficient of...

Three types of confidence intervals are developed for a general class of functionals of a survival distribution based on censored dependent data. The confidence intervals are constructed via asymptotic normality (Wald’s method), the empirical likelihood (EL) method, and the blockwise EL method...

Conditional efficiency captures efficiency of firms facing heterogeneous environmental conditions. Traditional approaches estimate nonparametrically conditional distribution requiring smoothing techniques. We rather use a flexible nonparametric location-scale model to eliminate the dependence of...

Frontier estimation appears in productivity analysis. Firm’s performance is measured by the distance between its output and an optimal production frontier. Frontier estimation becomes difficult if outputs are measured with noise and most approaches rely on restrictive parametric assumptions....

Results on asymptotic and finite sample properties of an estimator of a nonlinear regression function when errors are spatially correlated, and when the spatial dependence structure is unknown are derived. The proposed method is based on a generalized nonlinear least squares approach, taking...