空间盲源分离模型的潜在维度检验
Test of the Latent Dimension of a Spatial Blind Source Separation Model
AI总结：
针对含白噪声分量的空间盲源分离模型，提出白噪声分量数量检验方法及真实维度一致估计，其性能优于或不亚于bootstrap方法。
摘要：
我们假设存在一个空间盲源分离模型，其中观测到的多元空间数据是潜在的空间不相关高斯随机场的线性混合，这些随机场包含若干纯白噪声分量。我们提出了一种针对白噪声分量数量的检验方法，并推导了其统计量在一般区域上的渐近分布。我们还说明了在观测位置为网格状的情况下如何简化计算。基于该检验，我们得到了真实维度的一致估计量。模拟研究和一项环境应用表明，我们的检验方法至少与本文同样介绍的基于bootstrap的技术效果相当，且往往更优。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
