Some Distance-Based Tests of Independence for Sparsely-Sampled Multivariate Spatial Point Patterns
针对稀疏采样得到的多元空间点模式数据，提出几种新的距离基独立性检验方法，并通过林业数据实例和功效比较展示其有效性。
Summary Methods for the statistical analysis of spatial point patterns can be divided into two categories, according to whether or not a complete map of the underlying pattern is available. This paper is concerned only with methods for the analysis of data extracted from a pattern in situ by a 'sparse sampling' method involving the measurement of distances from sampling origins to neighbouring points of the pattern. The paper first gives a brief review of sparse sampling methods for univariate patterns and then discusses the problem of testing for independence between two or more patterns. Some new tests are described and power comparisons are presented. The methods are illustrated on two sets of forestry data, where the patterns are formed by the locations of trees of different species.