Multidimensional hazard estimation under generalized censoring

Abstract

This paper focuses on the problem of the estimation of the cumulative hazard function of a distribution on d-dimensional Euclidean space when the data points are subject to censoring by an arbitrary adapted random set. A problem involving observability of the estimator proposed in [8] and [9] is resolved and a functional central limit theorem is proven for the revised estimator. Several examples and applications are discussed, and the validity of bootstrap methods is established in each case.