|FSEM: Functional Structural Equation Models for Twin Functional Data.
|Year of Publication
|Luo, S, R Song, M Styner, J H. Gilmore, and H Zhu
|J Am Stat Assoc
The aim of this paper 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.
|J Am Stat Assoc
|FSEM: Functional structural equation models for twin functional data.
|PubMed Central ID
|R01 MH111944 / MH / NIMH NIH HHS / United States
R01 MH086633 / MH / NIMH NIH HHS / United States
R01 MH070890 / MH / NIMH NIH HHS / United States
R01 HD053000 / HD / NICHD NIH HHS / United States
U01 MH070890 / MH / NIMH NIH HHS / United States
P01 CA142538 / CA / NCI NIH HHS / United States
R01 MH092335 / MH / NIMH NIH HHS / United States