Advanced Statistical Models for Imaging and Genetic Data Public Deposited

Downloadable Content

Download PDF
Last Modified
  • March 22, 2019
Creator
  • Yu, Yang
    • Affiliation: College of Arts and Sciences, Department of Statistics and Operations Research
Abstract
  • The rapid growth of molecular biology and neuroimaging has facilitated many massive imaging genetics studies. These studies greatly advance our understanding of the development of neuropsychiatric and neurodegenerative disorders and their relationships to genetic biomarkers. Among the various collected structural and functional imaging data, we are particularly interested in those observed over time and/or space domains. One powerful type of statistical approach to analyze this kind of imaging data is based on functional data analysis techniques, including varying coefficient approaches and functional linear regression. This work improves functional data analysis approaches to imaging data by incorporating genetic information in the model which therefore improves explanatory and predictive power available from massive imaging genetics datasets. We propose two novel functional regression models for imaging genetics data, a semi-nonparametric varying coefficient model with functional response and a partially functional linear regression model with high-dimensional component. The performances of both models are assessed via extensive simulation studies. These methods are also applied to analyze real data collected in large imaging genetics studies such as the Alzheimer's Disease Neuroimaging Initiative and the Philadelphia Neurodevelopmental Cohort.
Date of publication
Keyword
Resource type
Rights statement
  • In Copyright
Advisor
  • Zhu, Hongtu
  • Ji, Chuanshu
  • Marron, James Stephen
  • Tran-Dinh, Quoc
  • Knickmeyer, Rebecca
Degree
  • Doctor of Philosophy
Degree granting institution
  • University of North Carolina at Chapel Hill
Graduation year
  • 2017
Language
Parents:

This work has no parents.

Items