Unsupervised Classification During Time-Series Model Building.
Unsupervised Classification During Time-Series Model Building.
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DOI：
10.1080/00273171.2016.1256187
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发表时间：
2017-03
影响因子：
3.8
通讯作者：
Guskiewicz K
中科院分区：
文献类型：
--
作者：
Gates KM;Lane ST;Varangis E;Giovanello K;Guskiewicz K
收集个体间多变量时间序列数据的研究人员必须决定是在个体水平还是在群体水平上对动态过程进行建模。最近的一项创新，组迭代多模型估计（GIMME），提供了一个解决方案，通过识别组级的时间序列模型，在数据驱动的方式，同时也可靠地恢复个人层面的动态影响模式，这种二分法。GIMME的独特之处在于，它不假设在时间效应的模式或权重方面个体之间的过程具有同质性。然而，很难从不同的个人层面模式的细微差别中做出推论。本文介绍了一种算法，到达具有类似的动态模型的个人的子组。重要的是，研究人员不需要决定亚组的数量。最终模型包含可靠的组，子组和个人级别的模式，使可推广的推论，具有共享模型功能的个人的子组，以及个人级别的模式和估计。我们表明，将社区检测集成到GIMME算法中，在两个重要方面改进了当前的标准：（1）提供可靠的分类和（2）增加个人水平效应恢复的可靠性。我们证明了这种方法的功能磁共振成像从前美国足球运动员的样本。
Researchers who collect multivariate time-series data across individuals must decide whether to model the dynamic processes at the individual level or at the group level. A recent innovation, group iterative multiple model estimation (GIMME), offers one solution to this dichotomy by identifying group-level time-series models in a data-driven manner while also reliably recovering individual-level patterns of dynamic effects. GIMME is unique in that it does not assume homogeneity in processes across individuals in terms of the patterns or weights of temporal effects. However, it can be difficult to make inferences from the nuances in varied individual-level patterns. The present article introduces an algorithm that arrives at subgroups of individuals that have similar dynamic models. Importantly, the researcher does not need to decide the number of subgroups. The final models contain reliable group-, subgroup-, and individual-level patterns that enable generalizable inferences, subgroups of individuals with shared model features, and individual-level patterns and estimates. We show that integrating community detection into the GIMME algorithm improves upon current standards in two important ways: (1) providing reliable classification and (2) increasing the reliability in the recovery of individual-level effects. We demonstrate this method on functional MRI from a sample of former American football players.
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DOI：
10.1098/rstb.2005.1641
发表时间：
2005-05-29
影响因子：
6.3
作者：
Eichler, M
通讯作者：
Eichler, M
影响因子：
4.3
作者：
Beltz AM;Molenaar PC
通讯作者：
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影响因子：
4.2
作者：
Chow, SM;Ram, N;Clore, G
通讯作者：
Clore, G
影响因子：
3.7
作者：
Gates KM;Molenaar PC;Iyer SP;Nigg JT;Fair DA
通讯作者：
Fair DA
影响因子：
3.5
作者：
Gates KM;Henry T;Steinley D;Fair DA
通讯作者：
Fair DA