论文标题
部分可观测时空混沌系统的无模型预测
Bayesian Models for Multivariate Difference Boundary Detection in Areal Data
论文作者
论文摘要
流行病学家广泛使用了划定的行政单位（例如，州，县，邮政编码）或面积单位的区域健康成果骨料，以绘制死亡率或发病率的幅度并捕获地理差异。为了捕捉对地区的健康差异，我们寻求“差异边界”，这些差异将邻近地区分开，其空间效应明显不同。在每个单位上的多个结果，我们在疾病和各个单位之间占据依赖性，这是更具挑战性的。在这里，我们解决了相关疾病的多元差边界检测。我们根据贝叶斯成对多重比较来提出问题，并寻求相邻空间效应的后验概率不同。为了实现这一目标，我们使用一类可容纳空间和疾病间依赖性依赖性的多元参考的Dirichlet工艺（MARDP）模型，将空间随机效应赋予离散概率定律（MARDP）模型。我们通过模拟研究评估我们的方法，并使用来自国家癌症研究所的监视，流行病学和最终结果（SEER）计划的数据来检测多种癌症的差异边界。
Regional aggregates of health outcomes over delineated administrative units (e.g., states, counties, zip codes), or areal units, are widely used by epidemiologists to map mortality or incidence rates and capture geographic variation. To capture health disparities over regions, we seek "difference boundaries" that separate neighboring regions with significantly different spatial effects. Matters are more challenging with multiple outcomes over each unit, where we capture dependence among diseases as well as across the areal units. Here, we address multivariate difference boundary detection for correlated diseases. We formulate the problem in terms of Bayesian pairwise multiple comparisons and seek the posterior probabilities of neighboring spatial effects being different. To achieve this, we endow the spatial random effects with a discrete probability law using a class of multivariate areally-referenced Dirichlet process (MARDP) models that accommodate spatial and inter-disease dependence. We evaluate our method through simulation studies and detect difference boundaries for multiple cancers using data from the Surveillance, Epidemiology, and End Results (SEER) Program of the National Cancer Institute.
