Motivated by a breast cancer research program, this paper is concerned with the joint survivor function of multiple event times when their observations are subject to informative censoring caused by a terminating event. We formulate the correlation of the multiple event times together with the time to the terminating event by an Archimedean copula to account for the informative censoring. Adapting the widely used two-stage procedure under a copula model, we propose an easy-to-implement pseudo-likelihood based procedure for estimating the model parameters. The approach yields a new estimator for the marginal distribution of a single event time with semicompeting-risks data. We conduct both asymptotics and simulation studies to examine the proposed approach in consistency, efficiency, and robustness. Data from the breast cancer program are employed to illustrate this research.
We develop a new dependent randomized rounding method for approximation of a number of optimization problems with integral
assignment constraints. The core of the method is a simple, intuitive, and computationally efficient geometric rounding that
simultaneously rounds multiple points in a multi-dimensional simplex to its vertices. Using this method we obtain in a systematic
way known as well as new results for the hub location, metric labeling, winner determination and consistent labeling problems.
A comprehensive comparison to the dependent randomized rounding method developed by Kleinberg and Tardos (J. ACM 49(5):616–639,
2002) and its variants is also conducted. Overall, our geometric rounding provides a simple and effective alternative for rounding
various integer optimization problems. 相似文献