The supLM test for structural change is embedded into a permutation test framework for a simple location model. The resulting conditional permutation distribution is compared to the usual (unconditional) asymptotic distribution, showing that the power of the test can be clearly improved in small samples. Furthermore, generalizations are discussed for binary and multivariate dependent variables as well as model-based permutation testing for structural change. The procedures suggested are illustrated using both artificial and real-world data (number of youth homicides, employment discrimination data, structural-change publications, and stock returns). / Series: Research Report Series / Department of Statistics and Mathematics

The R package coin implements a unified approach to permutation tests providing a huge class of independence tests for nominal, ordered, numeric, and censored data as well as multivariate data at mixed scales. Based on a rich and exible conceptual framework that embeds different permutation test procedures into a common theory, a computational framework is established in coin that likewise embeds the corresponding R functionality in a common S4 class structure with associated generic functions. As a consequence, the computational tools in coin inherit the exibility of the underlying theory and conditional inference functions for important special cases can be set up easily. Conditional versions of classical tests|such as tests for location and scale problems in two or more samples, independence in two- or three-way contingency tables, or association problems for censored, ordered categorical or multivariate data|can easily be implemented as special cases using this computational toolbox by choosing appropriate transformations of the observations. The paper gives a detailed exposition of both the internal structure of the package and the provided user interfaces along with examples on how to extend the implemented functionality. (authors' abstract)

The R package coin implements a unified approach to permutation tests providing a huge class of independence tests for nominal, ordered, numeric, and censored data as well as multivariate data at mixed scales. Based on a rich and flexible conceptual framework that embeds different permutation test procedures into a common theory, a computational framework is established in coin that likewise embeds the corresponding R functionality in a common S4 class structure with associated generic functions. As a consequence, the computational tools in coin inherit the flexibility of the underlying theory and conditional inference functions for important special cases can be set up easily. Conditional versions of classical tests - such as tests for location and scale problems in two or more samples, independence in two- or three-way contingency tables, or association problems for censored, ordered categorical or multivariate data - can be easily be implemented as special cases using this computational toolbox by choosing appropriate transformations of the observations. The paper gives a detailed exposition of both the internal structure of the package and the provided user interfaces. / Series: Research Report Series / Department of Statistics and Mathematics

第一章為緒論。 第二章為多變量常態分配、球狀常態分配、威夏特（WISHART） 分配以及最大數化的 基本概念。 第三章為球狀檢定，討論其檢定的不變性、一火偏性以及在Ｈ下的動差和正合（EXAC T）分配，最後以Ｕ─１檢定步驟來驗證球狀檢定。 第四章為一些均勻性變當數方法和球狀聯立檢定，將分別介紹Ｌ□檢定、Ｍ檢定以及 八種球狀聯立檢定之方法。 第五章為主畏成份檢定，將介紹一些最後Ｋ個特徵值相等的檢定方法以及實例分析。 第六章為結論，並說明其進一步研究的方向。