Simultaneous confidence corridors for mean functions in functional data analysis of imaging data
Simultaneous confidence corridors for mean functions in functional data analysis of imaging data
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DOI：
10.1111/biom.13156
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发表时间：
2019-11
期刊：
影响因子：
1.9
通讯作者：
Yueying Wang;Guannan Wang;Li Wang;R. Ogden
中科院分区：
文献类型：
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作者：
Yueying Wang;Guannan Wang;Li Wang;R. Ogden
最近的工作涉及的生物医学成像数据的分析的动机，我们提出了一种新的程序，同时构建成像数据的平均值的置信走廊。我们建议使用灵活的二元样条三角剖分处理不规则域的图像，这是常见的脑成像研究和其他生物医学成像应用。在一定的正则性条件下，证明了均值函数的样条估计的相合性和渐近正态性。我们还提供了一个计算效率的协方差函数的估计，并推导出其一致的一致性。该程序也扩展到两个样本的情况下，我们专注于比较平均函数从两个群体的成像数据。通过Monte Carlo模拟研究，我们检验了所提出的方法的有限样本性能。最后，将所提出的方法应用于分析两个不同研究中的脑正电子发射断层扫描数据。在准备这篇文章中使用的一个数据集是从阿尔茨海默病神经影像倡议（ADNI）数据库中获得的。
Motivated by recent work involving the analysis of biomedical imaging data, we present a novel procedure for constructing simultaneous confidence corridors for the mean of imaging data. We propose to use flexible bivariate splines over triangulations to handle an irregular domain of the images that is common in brain imaging studies and in other biomedical imaging applications. The proposed spline estimators of the mean functions are shown to be consistent and asymptotically normal under some regularity conditions. We also provide a computationally efficient estimator of the covariance function and derive its uniform consistency. The procedure is also extended to the two‐sample case in which we focus on comparing the mean functions from two populations of imaging data. Through Monte Carlo simulation studies, we examine the finite sample performance of the proposed method. Finally, the proposed method is applied to analyze brain positron emission tomography data in two different studies. One data set used in preparation of this article was obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database.