Massively parallel nonparametric regression, with an application to developmental brain mapping.
Massively parallel nonparametric regression, with an application to developmental brain mapping.
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大规模并行非参数回归，应用于发育性大脑绘图。
DOI：
10.1080/10618600.2012.733549
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
Mennes,Maarten
中科院分区：
文献类型：
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作者：
Reiss,PhilipT;Huang,Lei;Chen,Yin-Hsiu;Huo,Lan;Tarpey,Thaddeus;Mennes,Maarten
一个惩罚的方法，提出了执行大量的并行非参数分析的两种类型之一：限制似然比检验的参数回归模型与一般平滑的替代，和非参数回归。与简单地依次执行每个分析相比，我们的技术大大减少了计算时间。查看我们的方法产生的散点图平滑功能数据的大集合，我们开发了一个聚类方法来总结和可视化这些结果。我们的方法适用于超高维数据，特别是通过神经成像获得的数据;我们通过分析大约70，000个大脑位置中每个位置的功能连接的发展轨迹来说明它。补充材料，包括附录和R包，可在线获得。
A penalized approach is proposed for performing large numbers of parallel nonparametric analyses of either of two types: restricted likelihood ratio tests of a parametric regression model versus a general smooth alternative, and nonparametric regression. Compared with naïvely performing each analysis in turn, our techniques reduce computation time dramatically. Viewing the large collection of scatterplot smooths produced by our methods as functional data, we develop a clustering approach to summarize and visualize these results. Our approach is applicable to ultra-high-dimensional data, particularly data acquired by neuroimaging; we illustrate it with an analysis of developmental trajectories of functional connectivity at each of approximately 70,000 brain locations. Supplementary materials, including an appendix and an R package, are available online.