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21	排列檢定法應用於空間資料之比較 / Permutation test on spatial comparison 王信忠, Wang, Hsin-Chung Unknown Date (has links) 本論文主要是探討在二維度空間上二母體分佈是否一致。我們利用排列 (permutation)檢定方法來做比較, 並藉由費雪(Fisher)正確檢定方法的想法而提出重標記 (relabel)排列檢定方法或稱為費雪排列檢定法。我們透過可交換性的特質證明它是正確 (exact) 的並且比 Syrjala (1996)所建議的排列檢定方法有更高的檢定力 (power)。本論文另提出二個空間模型: spatial multinomial-relative-log-normal 模型與 spatial Poisson-relative-log-normal 模型來配適一般在漁業中常有的右斜長尾次數分佈並包含很多0 的空間資料。另外一般物種可能因天性或自然環境因素像食物、溫度等影響而有群聚行為發生, 這二個模型亦可描述出空間資料的群聚現象以做適當的推論。 / This thesis proposes the relabel (Fisher's) permutation test inspired by Fisher's exact test to compare between distributions of two (fishery) data sets locating on a two-dimensional lattice. We show that the permutation test given by Syrjala (1996} is not exact, but our relabel permutation test is exact and, additionally, more powerful. This thesis also studies two spatial models: the spatial multinomial-relative-log-normal model and the spatial Poisson-relative-log-normal model. Both models not only exhibit characteristics of skewness with a long right-hand tail and of high proportion of zero catches which usually appear in fishery data, but also have the ability to describe various types of aggregative behaviors. 費雪(Fisher)正確檢定 Cramer-von Mises 統計量排列檢定可交換性空間分佈貝氏(Bayesian)方法檢定力比較空間自我迴歸(CAR)模型 auto-Poisson模型 auto-Gaussian模型群聚 Fisher's exact test Cramer-von Mises statistic permutation test exchangeable spatial distributions Bayesian approach power comparison auto-Poisson model auto-Gaussian model cluster

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排列檢定法應用於空間資料之比較 / Permutation test on spatial comparison