论文标题
各种分解分解面积计数数据
Multiresolution Decomposition of Areal Count Data
论文作者
论文摘要
多分辨率分解通常被理解为在随机信号中捕获比例依赖性特征的过程。首先建立了用于图像处理的方法，通常依赖于栅格或定期网格数据。在本文中，我们将特定的多分辨率分解过程扩展到了面积计数数据，即〜离散不规则的网格数据。更具体地说，我们将所谓的besag-york-mollié模型的新模型概念和分布纳入其中，以包括先验人口统计学知识。下面仔细概述了计算方案中的这些适应和后续变化，而原始多解决分解的主要思想仍然存在。最后，我们通过将其应用于德国的口腔癌症来显示扩展的可行性。
Multiresolution decomposition is commonly understood as a procedure to capture scale-dependent features in random signals. Such methods were first established for image processing and typically rely on raster or regularly gridded data. In this article, we extend a particular multiresolution decomposition procedure to areal count data, i.e.~discrete irregularly gridded data. More specifically, we incorporate in a new model concept and distributions from the so-called Besag--York--Mollié model to include a priori demographical knowledge. These adaptions and subsequent changes in the computation schemes are carefully outlined below, whereas the main idea of the original multiresolution decomposition remains. Finally, we show the extension's feasibility by applying it to oral cavity cancer counts in Germany.
