Inference in functional mixed regression models with applications to Positron Emission Tomography imaging data.
Inference in functional mixed regression models with applications to Positron Emission Tomography imaging data.
复制标题
功能混合回归模型的推理及其在正电子发射断层扫描成像数据中的应用。
DOI：
10.1002/sim.9087
复制
发表时间：
2021
影响因子：
2
通讯作者：
Ogden,RTodd
中科院分区：
文献类型：
--
作者：
Shi,Baoyi;Ogden,RTodd
在函数-标量回归框架中，我们提出了一些函数混合模型的建模策略，以及一些对固定效应的各个方面进行推断的方法。这是在建模的背景下提出的正电子发射断层扫描（PET）数据，以探索整个人类大脑的各种蛋白质的密度。对于这种应用，关于给定脑区域中靶蛋白密度的信息被封装在该区域的脉冲响应函数（IRF）中。以前的非参数估计的IRF的工作是有限的，因为它只能模拟一个单一的大脑区域的时间。我们提出了一个扩展，基于功能数据分析的原则，这将允许同时对多个大脑区域进行建模。更广泛地适用于功能混合回归建模，我们讨论了两种一般的排列测试方法，并描述了有效的策略，用于识别模型内的可交换单元，并建立相应的排列测试。我们说明我们的方法与PET数据的应用程序，并探讨抑郁症和性别对IRF的影响。
In a function‐on‐scalar regression framework, we present some modeling strategies for functional mixed models and also some approaches for making inference about various aspects of the fixed effects. This is presented in the context of modeling positron emission tomography (PET) data in order to explore the density of various proteins of interest throughout the human brain. For this application, information about the density of the target protein in a given brain region is encapsulated in theimpulse response function(IRF) of the region. Previous work on nonparametric estimation of the IRF is limited in that it is only able to model a single brain region at a time. We propose an extension, based on principles of functional data analysis, that will allow modeling of multiple brain regions simultaneously. Applicable more broadly to functional mixed regression modeling, we discuss two general approaches for permutation testing and describe valid strategies for identifying exchangeable units within the model and building corresponding permutation tests. We illustrate our methods with an application to PET data and explore the effects of depression and sex on the IRF.