Statistical analyses for ranking data

a）微阵列数据的途径分析。 微阵列数据的统计分析可以潜在地为标记在复杂疾病的发展中发挥重要生物学作用的基因提供重要信息。在这项研究中，我们计划在初始阶段通过关注微阵列数据的途径或功能富集来纳入生物学，以考虑有意义的基因分组。对途径的分析可以帮助确定一组基因表达的微妙变化，对这些变化的生物学解释更明显。所提出的方法是完全非参数的性质，并考虑到基因之间自然存在的相关性。 B）具有伞形排序的两个组的比较。 在最近的一篇文章中，开发和评价了一种通用方法，用于构建“伞型替代”假设的非参数检验。这种替代方案出现在治疗效果在达到峰值后改变方向的情况下。在本研究中，我们比较了两个人口表现出伞排序和发展的部分假设，他们有相同的峰值，无论其位置是已知的测试。 c）基于数据等级的非参数分类问题。 在这项研究中，我们对待众所周知的分类问题，涉及两个或两个以上的人口，但建议只使用排名的数据。

a) Pathway analysis of microarray data. The statistical analysis of microarray data can potentially provide vital information for flagging genes that play important biological roles in the development of a complex disease. In this research, we plan to incorporate the biology at the initial stage by focusing on pathways or functional enrichment of microarray data as a way to take into account meaningful groupings of genes. An analysis of pathways can help to identify subtle changes in the expression of a group of genes for which the biological interpretation is more apparent. The approach being proposed is entirely nonparametric in nature and takes into account correlations which naturally exist among genes. b) The comparison of two groups having an umbrella ordering. In a recent article, a general method was developed and evaluated for constructing nonparametric tests of hypotheses for "umbrella alternatives". Such alternatives arise in situations where the treatment effect changes in direction after reaching a peak. In the present research, we compare two populations which exhibit an umbrella ordering and develop in part a test of the hypothesis that they have the same peak, whether or not its location is known. c) Nonparametric classification problems based on the ranks of the data. In this research, we treat the well-known classification problem involving two or more populations but propose to use only the ranks of the data.