Anton Schick

TL;DR: In this article, the authors consider an interval censorship model in which the endpoints of the censoring intervals are determined by a two-stage experiment and prove the strong consistency in the L 1 (μ)-topology of the nonparametric maximum likelihood estimate of the underlying survival function for a measure μ which is derived from the distributions of the end points.

TL;DR: In this article, the density of a sum of independent random variables can be estimated by the convolution of kernel estimators for the marginal densities, and the resulting estimator is n 1/2-consistent and converges in distribution in the spaces C 0(ℝ) and L 1 to a centered Gaussian process.

TL;DR: In this paper, the problem of testing the equality of two nonparametric regression curves against one-sided alternatives when the design points are common and when they are distinct is discussed, and two classes of tests are given for each case.

TL;DR: In this paper, a new density estimator that converges, in the supremum norm, at the better, parametric, rate n -1/2 is presented. But this estimator is a convolution of two different residual-based kernel estimators.