Group Testing for Longitudinal Data
Group Testing for Longitudinal Data
复制标题
纵向数据的分组测试
DOI：
10.1007/978-3-319-19992-4_11
复制
发表时间：
2015
期刊：
影响因子：
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通讯作者：
M. Niethammer
中科院分区：
文献类型：
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作者：
Yi Hong;Nikhil Singh;R. Kwitt;M. Niethammer
我们考虑如何测试的形状给定的纵向数据的组差异。特别是，我们感兴趣的是各组的主题的纵向模型的差异。本文将主测地线分析推广到形空间的切丛上。这允许估计总结纵向数据内的形状变化的轨迹的分布的方差和主方向。每个轨迹都被参数化为切丛中的一个点。为了研究两种轨迹分布的统计差异，我们将欧氏空间中的Bhattacharyya距离推广到切丛上。这不仅允许考虑二阶统计量，而且还可以作为我们在排列测试期间的测试统计量。我们的方法在合成和真实的数据上进行了验证，实验结果表明，在识别组差异方面，统计能力有所提高。事实上，我们的研究揭示了老年痴呆症受试者与正常对照者胼胝体纵向形状的组间差异。
We consider how to test for group differences of shapes given longitudinal data. In particular, we are interested in differences of longitudinal models of each group's subjects. We introduce a generalization of principal geodesic analysis to the tangent bundle of a shape space. This allows the estimation of the variance and principal directions of the distribution of trajectories that summarize shape variations within the longitudinal data. Each trajectory is parameterized as a point in the tangent bundle. To study statistical differences in two distributions of trajectories, we generalize the Bhattacharyya distance in Euclidean space to the tangent bundle. This not only allows to take second-order statistics into account, but also serves as our test-statistic during permutation testing. Our method is validated on both synthetic and real data, and the experimental results indicate improved statistical power in identifying group differences. In fact, our study sheds new light on group differences in longitudinal corpus callosum shapes of subjects with dementia versus normal controls.