Draxler and Zessin [1] derived the power function for a class of conditional tests of assumptions of a psychometric model known as the Rasch model and suggested an MCMC approach developed by Verhelst [2] for the numer...Draxler and Zessin [1] derived the power function for a class of conditional tests of assumptions of a psychometric model known as the Rasch model and suggested an MCMC approach developed by Verhelst [2] for the numerical approximation of the power of the tests. In this contribution, the precision of the Verhelst approach is investigated and compared with an exact sampling procedure proposed by Miller and Harrison [3] for which the discrete probability distribution to be sampled from is exactly known. Results show no substantial differences between the two numerical procedures and quite accurate power computations. Regarding the question of computing time the Verhelst approach will have to be considered much more efficient.展开更多
We study the tail probability of the stationary distribution of nonparametric nonlinear autoregressive functional conditional heteroscedastic (NARFCH) model with heavytailed innovations. Our result shows that the tail...We study the tail probability of the stationary distribution of nonparametric nonlinear autoregressive functional conditional heteroscedastic (NARFCH) model with heavytailed innovations. Our result shows that the tail of the stationary marginal distribution of an NARFCH series is heavily dependent on its conditional variance. When the innovations are heavy-tailed, the tail of the stationary marginal distribution of the series will become heavier or thinner than that of its innovations. We give some specific formulas to show how the increment or decrement of tail heaviness depends on the assumption on the conditional variance function. Some examples are given.展开更多
将状态检修与大面积停电问题结合起来,提出一种基于状态检修(condition based maintenance,CBM)的N-k故障在线辨识方法:根据状态检修实时数据建立设备的故障可能性模型,应用功能组(functional group,FG)理论及相关改进后的搜索方法实现...将状态检修与大面积停电问题结合起来,提出一种基于状态检修(condition based maintenance,CBM)的N-k故障在线辨识方法:根据状态检修实时数据建立设备的故障可能性模型,应用功能组(functional group,FG)理论及相关改进后的搜索方法实现N-k故障路径的在线搜索及筛选,并根据电网实际运行方式以及状态检修的监测情况,建立了N-k故障的概率模型。最后,利用C++语言实现了所提方法,并以安阳电网的3个变电站为例验证了方法的正确性。展开更多
软件测试中收集的累积出错数据,往往是不完全的,使用它们对软件的可靠性进行分析,将影响到分析结果的精度.解决这一问题的途径有很多,本文试图应用 EM 算法于 NHPP(非齐次泊松过程)类模型的参数估计,以提高估计精度,从而提高软件可靠性...软件测试中收集的累积出错数据,往往是不完全的,使用它们对软件的可靠性进行分析,将影响到分析结果的精度.解决这一问题的途径有很多,本文试图应用 EM 算法于 NHPP(非齐次泊松过程)类模型的参数估计,以提高估计精度,从而提高软件可靠性分析的精确程度.展开更多
文摘Draxler and Zessin [1] derived the power function for a class of conditional tests of assumptions of a psychometric model known as the Rasch model and suggested an MCMC approach developed by Verhelst [2] for the numerical approximation of the power of the tests. In this contribution, the precision of the Verhelst approach is investigated and compared with an exact sampling procedure proposed by Miller and Harrison [3] for which the discrete probability distribution to be sampled from is exactly known. Results show no substantial differences between the two numerical procedures and quite accurate power computations. Regarding the question of computing time the Verhelst approach will have to be considered much more efficient.
基金supported by the National Natural Science Foundation of China(Grant No.10471005).
文摘We study the tail probability of the stationary distribution of nonparametric nonlinear autoregressive functional conditional heteroscedastic (NARFCH) model with heavytailed innovations. Our result shows that the tail of the stationary marginal distribution of an NARFCH series is heavily dependent on its conditional variance. When the innovations are heavy-tailed, the tail of the stationary marginal distribution of the series will become heavier or thinner than that of its innovations. We give some specific formulas to show how the increment or decrement of tail heaviness depends on the assumption on the conditional variance function. Some examples are given.
文摘将状态检修与大面积停电问题结合起来,提出一种基于状态检修(condition based maintenance,CBM)的N-k故障在线辨识方法:根据状态检修实时数据建立设备的故障可能性模型,应用功能组(functional group,FG)理论及相关改进后的搜索方法实现N-k故障路径的在线搜索及筛选,并根据电网实际运行方式以及状态检修的监测情况,建立了N-k故障的概率模型。最后,利用C++语言实现了所提方法,并以安阳电网的3个变电站为例验证了方法的正确性。
