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Groupwise envelope models for imaging genetic analysis
Author(s) -
Park Yeonhee,
Su Zhihua,
Zhu Hongtu
Publication year - 2017
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/biom.12689
Subject(s) - imaging genetics , neuroimaging , multivariate statistics , envelope (radar) , computer science , covariate , data set , set (abstract data type) , multivariate analysis , regression , alzheimer's disease neuroimaging initiative , artificial intelligence , linear model , regression analysis , machine learning , data mining , computational biology , disease , alzheimer's disease , statistics , medicine , biology , mathematics , neuroscience , programming language , telecommunications , radar
Summary Motivated by searching for associations between genetic variants and brain imaging phenotypes, the aim of this article is to develop a groupwise envelope model for multivariate linear regression in order to establish the association between both multivariate responses and covariates. The groupwise envelope model allows for both distinct regression coefficients and distinct error structures for different groups. Statistically, the proposed envelope model can dramatically improve efficiency of tests and of estimation. Theoretical properties of the proposed model are established. Numerical experiments as well as the analysis of an imaging genetic data set obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study show the effectiveness of the model in efficient estimation. Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database.