Reduction Algorithm for the NPMLE for the Distribution Function of Bivariate Interval-Censored Data

摘要

This article considers computational aspects of the nonparametric maximum likelihood estimator (NPMLE) for the distribution function of bivariate interval-censored data. The computation of the NPMLE consists of a parameter reduction step and an optimization step. This article focuses on the reduction step and introduces two new reduction algorithms: the Tree algorithm and the HeightMap algorithm. The Tree algorithm is mentioned only briefly. The HeightMap algorithm is discussed in detail and also given in pseudo code. It is a fast and simple algorithm of time complexity O(n~2). This is an order faster than the best known algorithm thus far by Bogaerts and Lesaffre. We compare the new algorithms to earlier algorithms in a simulation study, and demonstrate that the new algorithms are significantly faster. Finally, we discuss how the HeightMap algorithm can be generalized to d-dimensional data with d > 2. Such a multivariate version of the HeightMap algorithm has time complexity O(n~d).