Because the causes of data missing were more complex. In different applications, data generation process was different. So people usually supposed the data are collected according to the ideal state at the time of the design and development. 因为数据缺失产生的原因比较复杂,不同的应用中数据生成的过程迥异,所以算法开发和设计的时候通常假设数据是按照理想状态收集的。
These methods are better able to optimize the slice data generation process, to improve the stereo image effects, and improve the efficiency of data generation. 这些方法能够更好的优化切片数据生成过程(DGP),提高立体图像效果,并提高数据生成效率。
The results showed that in small sample cases, the structural breaks in data generation process has no affect to the bootstrap cointegration test. 结果表明,在小样本情况下,自举协整检验方法的应用就降低了数据生成过程(DGP)存在的变结构对协整检验功效的影响。
The application of statistical methods has been more or less certain restrictions from the data structure, missing data or data generation process, in the face of ordinal data, such restrictions more and more complex. 统计方法的应用实践经常会受到来自数据结构、数据缺失或者数据生成过程(DGP)的某些限制,在面对有序数据时,这种限制更多,也更复杂。
These fluctuations and some relative policies with anti-cycle characters had been carried into execution, which may lead to structural change of the data generation process of economic variables ( e.g.price, money supply ). 这些波动及相应的带有反周期特征的政策的实施,均可能使价格、货币供给等经济变量的数据生产过程发生结构突变。