The asymptotic normality of internal estimator for nonparametric regression

Asymptotic normality of a nonparametric estimator of sample coverage

This paper establishes a necessary and sufficient condition for the asymptotic normality of the nonparametric estimator of sample coverage proposed by Good [Biometrica 40 (1953) 237–264]. This new necessary and sufficient condition extends the validity of the asymptotic normality beyond the previously proven cases.

Asymptotic Normality of a Nonparametric Conditional Quantile Estimator for Random Fields

Asymptotic normality of Powell’s kernel estimator

In this paper, we establish asymptotic normality of Powell’s kernel estimator for the asymptotic covariance matrix of the quantile regression estimator for both i.i.d. and weakly dependent data. As an application, we derive the optimal bandwidth that minimizes the approximate mean squared error of the kernel estimator.

Asymptotic Equivalence for Nonparametric Regression

We consider a nonparametric model E, generated by independent observations Xi, i = 1, ..., n, with densities p(x, θi), i = 1, ..., n, the parameters of which θi = f(i/n) ∈ Θ are driven by the values of an unknown function f : [0, 1]→ Θ in a smoothness class. The main result of the paper is that, under regularity assumptions, this model can be approximated, in the sense of the Le Cam deficiency ...

asymptotic normality of the truncation probability estimator for truncated dependent data

in some long term studies, a series of dependent and possibly truncated life-times may be observed. suppose that the lifetimes have a common marginal distribution function. in left-truncation model, one observes data (xi,ti) only, when ti ≤ xi. under some regularity conditions, we provide a strong representation of the ßn estimator of ß = p(ti ≤ xi), in the form of an average of random variable...