Consider the nonparametric regression model Y = m(X)+e, where the function m is smooth, but unknown.We construct tests for the independence of e and X, based on n independent copies of (X; Y ).The testing procedures are based on differences of neighboring Y 's.We establish asymptotic results for...

AMS classifications: 62G08, 62G10, 62G20, 62G30; 60F17.

Consider the location-scale regression model Y = m(X)+o(X), where the error e is independent of the covariate X, and m and o are smooth but unknown functions.We construct tests for the validity of this model and show that the asymptotic limits of the proposed test statistics are distribution...

Several bandwidth selection procedures for kernel density estimation of a random variable that is sampled under random double truncation are introduced and compared. The motivation is based on the fact that this type of incomplete data is often encountered in astronomy and medicine. The...

We consider a nonparametric regression model where the response Y and the covariate X are both functional (i.e. valued in some infinite-dimensional space). We define a kernel type estimator of the regression operator and we first establish its pointwise asymptotic normality. The double...

AMS classifications: 62G08, 62G10, 62G20, 62G30; 60F17.