论文标题
命令限制自适应逐步审查竞争风险数据的推论数据
Order Restricted Inference for Adaptive Progressively Censored Competing Risks Data
论文作者
论文摘要
储层计算是预测湍流的有力工具，其简单的架构具有处理大型系统的计算效率。然而，其实现通常需要完整的状态向量测量和系统非线性知识。我们使用非线性投影函数将系统测量扩展到高维空间，然后将其输入到储层中以获得预测。我们展示了这种储层计算网络在时空混沌系统上的应用，该系统模拟了湍流的若干特征。我们表明，使用径向基函数作为非线性投影器，即使只有部分观测并且不知道控制方程，也能稳健地捕捉复杂的系统非线性。最后，我们表明，当测量稀疏、不完整且带有噪声，甚至控制方程变得不准确时，我们的网络仍然可以产生相当准确的预测，从而为实际湍流系统的无模型预测铺平了道路。
Under adaptive progressive Type-II censoring schemes, order restricted inference based on competing risks data is discussed in this article. The latent failure lifetimes for the competing causes are assumed to follow Weibull distributions, with an order restriction on the scale parameters of the distributions. The practical implication of this order restriction is that one of the risk factors is dominant, as often observed in competing risks scenarios. In this setting, likelihood estimation for the model parameters, along with bootstrap based techniques for constructing asymptotic confidence intervals are presented. Bayesian inferential methods for obtaining point estimates and credible intervals for the model parameters are also discussed. Through a detailed Monte Carlo simulation study, the performance of order restricted inferential methods are assessed. In addition, the results are also compared with the case when no order restriction is imposed on the estimation approach. The simulation study shows that order restricted inference is more efficient between the two, when this additional information is taken into consideration. A numerical example is provided for illustrative purpose.
