Asymptotic expansions for statistics computed from spatial data
Asymptotic expansions for statistics computed from spatial data
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根据空间数据计算的统计量的渐近展开
DOI：
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发表时间：
1996
期刊：
影响因子：
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通讯作者：
P. García
中科院分区：
文献类型：
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作者：
P. García
摘要将依赖数据的Edgeworth展开式推广到空间模式的背景下，目的是得到近似由弱依赖覆盖过程产生的空间数据计算的统计量分布的渐近展开式。特别地，对未被过程覆盖的区域的预期比例（其孔隙率）的估计进行了详细的处理，并在布尔模型的上下文中给出了显式公式，假设生成模型的随机集本质上是有界的，并且满足cramsamrs条件的一个版本。
SummaryThe Edgeworth expansions for dependent data are generalized to the context of spatial patterns, with the aim of obtaining asymptotic expansions which approximate the distribution of statistics computed from spatial data, generated by a weakly dependent coverage process. In particular, the case of estimating the expected proportion (its porosity) of a region that is not covered by the process is treated in detail and explicit formulae are given in the context of a Boolean model, assuming that the random sets generating the model are essentially bounded and satisfy a version of Cramér’s condition.