FSEM: Functional Structural Equation Models for Twin Functional Data
FSEM: Functional Structural Equation Models for Twin Functional Data
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DOI：
10.1080/01621459.2017.1407773
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发表时间：
2019-01-02
影响因子：
3.7
通讯作者：
Zhu, H.
中科院分区：
文献类型：
--
作者：
Luo, S.;Song, R.;Zhu, H.
本文的目的是开发一类新的函数结构方程模型(FSEM)，用于分析函数遗传和环境对孪生函数数据的影响，同时表征函数数据和感兴趣的协变量之间的变化关联。我们提出了一个三阶段估计方法来估计不同协变量(如性别)的变化系数函数以及三个用于遗传和环境效应的协方差算子。我们开发了一种基于加权似然比统计量的推理程序，以检验固定位置或紧凑区域的遗传/环境效应。系统地对估计变差函数、加权似然比统计量和估计协方差算子进行了理论分析。我们进行了广泛的蒙特卡罗模拟来检验估计和推断过程的有限样本性能。我们应用建议的FSEM来量化遗传和环境对双胞胎白质束的影响程度，这些影响来自北卡罗来纳大学早期大脑发育研究。这篇文章的补充材料可以在网上找到。
The aim of this article is to develop a novel class of functional structural equation models (FSEMs) for dissecting functional genetic and environmental effects on twin functional data, while characterizing the varying association between functional data and covariates of interest. We propose a three-stage estimation procedure to estimate varying coefficient functions for various covariates (e.g., gender) as well as three covariance operators for the genetic and environmental effects. We develop an inference procedure based on weighted likelihood ratio statistics to test the genetic/environmental effect at either a fixed location or a compact region. We also systematically carry out the theoretical analysis of the estimated varying functions, the weighted likelihood ratio statistics, and the estimated covariance operators. We conduct extensive Monte Carlo simulations to examine the finite-sample performance of the estimation and inference procedures. We apply the proposed FSEM to quantify the degree of genetic and environmental effects on twin white matter tracts obtained from the UNC early brain development study. Supplementary materials for this article are available online.