Publication at Faculty of Mathematics and Physics |
2019
Abstract
We study nonparametric estimation of the length distribution for stationary line segment processes in the d-dimensional Euclidean space. We review different approaches (Horvitz-Thompson type estimator, reduced-sample estimator, Kaplan-Meier estimator, nonparametric maximum likelihood estimator, stochastic restoration estimation) and compare the finite sample behaviour by means of a simulation study for stationary line segment processes in 2D and 3D.