论文标题
从离散观测的扩散过程中的因子分析中的统计推断
Statistical inference in factor analysis for diffusion processes from discrete observations
论文作者
论文摘要
我们考虑了从离散观测值中的厄运和非共性扩散过程中的因子分析中的统计推论。基于高频时间序列数据的因子模型主要在高维协方差矩阵估计的领域进行了讨论。在该领域，主要使用了基于主成分分析的方法。但是，此方法仅对高维模型有效。另一方面，有一种基于准类的方法。但是，由于假定因子是可观察到的，因此当因子是潜在的时，我们不能使用此方法。因此，当因子是潜在的，并且可观察变量的尺寸不高时，现有方法无效。因此，我们在这种情况下提出了一种有效的方法。
We consider statistical inference in factor analysis for ergodic and non-ergodic diffusion processes from discrete observations. Factor model based on high frequency time series data has been mainly discussed in the field of high dimensional covariance matrix estimation. In this field, the method based on principal component analysis has been mainly used. However, this method is effective only for high dimensional model. On the other hand, there is a method based on the quasi-likelihood. However, since the factor is assumed to be observable, we cannot use this method when the factor is latent. Thus, the existing methods are not effective when the factor is latent and the dimension of the observable variable is not so high. Therefore, we propose an effective method in the situation.
