论文标题
空间点模式中点标志性核心依赖性结构的非参数测试
Nonparametric testing of the dependence structure among points-marks-covariates in spatial point patterns
论文作者
论文摘要
我们研究了在明显的点过程中协变量和标记之间独立性假设的测试。如果（未标记）过程独立于协变量和标记，那将是相当简单的。但是，在实践中，这种假设是值得怀疑的，并且点过程与协变量之间的可能依赖性可能导致结论不正确。因此，我们建议研究三角形点中的完整依赖性结构 - 标记 - 共核心。我们利用了非参数随机移位方法的最新发展，即新的方差校正方法，并提出了标记与协变量之间以及点和协变量之间独立性无效假设的测试。我们提出了一项详细的仿真研究，显示了方法的性能，并提供了两个定理，以建立适当的校正因子以进行方差校正。最后，我们说明了在两个实际应用中使用所提出的方法的使用。
We investigate testing of the hypothesis of independence between a covariate and the marks in a marked point process. It would be rather straightforward if the (unmarked) point process were independent of the covariate and the marks. In practice, however, such an assumption is questionable and possible dependence between the point process and the covariate or the marks may lead to incorrect conclusions. Therefore, we propose to investigate the complete dependence structure in the triangle points--marks--covariates together. We take advantage of the recent development of the nonparametric random shift methods, namely the new variance correction approach, and propose tests of the null hypothesis of independence between the marks and the covariate and between the points and the covariate. We present a detailed simulation study showing the performance of the methods and provide two theorems establishing the appropriate form of the correction factors for the variance correction. Finally, we illustrate the use of the proposed methods in two real applications.
