论文标题
测试空间盲源分离模型的潜在维度
Test of the Latent Dimension of a Spatial Blind Source Separation Model
论文作者
论文摘要
我们假设一个空间盲源分离模型，其中观察到的多元空间数据是潜在空间不相关的高斯随机场的线性混合物，其中包含许多纯白噪声组件。我们提出了对白噪声组件数量的测试，并获得其对通用域的统计数据的渐近分布。我们还证明了在网格观察位置的情况下如何促进计算。基于此测试，我们获得了真实维度的一致估计器。仿真研究和环境应用表明，我们的测试至少与基于自举的技术相当，并且通常超过了基于引导的技术，这也是本文引入的。
We assume a spatial blind source separation model in which the observed multivariate spatial data is a linear mixture of latent spatially uncorrelated Gaussian random fields containing a number of pure white noise components. We propose a test on the number of white noise components and obtain the asymptotic distribution of its statistic for a general domain. We also demonstrate how computations can be facilitated in the case of gridded observation locations. Based on this test, we obtain a consistent estimator of the true dimension. Simulation studies and an environmental application demonstrate that our test is at least comparable to and often outperforms bootstrap-based techniques, which are also introduced in this paper.
