Multiscale adaptive generalized estimating equations for longitudinal neuroimaging data
نویسندگان
چکیده
منابع مشابه
Multiscale adaptive generalized estimating equations for longitudinal neuroimaging data
Many large-scale longitudinal imaging studies have been or are being widely conducted to better understand the progress of neuropsychiatric and neurodegenerative disorders and normal brain development. The goal of this article is to develop a multiscale adaptive generalized estimation equation (MAGEE) method for spatial and adaptive analysis of neuroimaging data from longitudinal studies. MAGEE...
متن کاملPenalized generalized estimating equations for high-dimensional longitudinal data analysis.
We consider the penalized generalized estimating equations (GEEs) for analyzing longitudinal data with high-dimensional covariates, which often arise in microarray experiments and large-scale health studies. Existing high-dimensional regression procedures often assume independent data and rely on the likelihood function. Construction of a feasible joint likelihood function for high-dimensional ...
متن کاملUsing Generalized Estimating Equations for Longitudinal Data Analysis
The generalized estimating equation (GEE) approach of Zeger and Liang facilitates analysis of data collected in longitudinal, nested, or repeated measures designs. GEEs use the generalized linear model to estimate more efficient and unbiased regression parameters relative to ordinary least squares regression in part because they permit specification of a working correlation matrix that accounts...
متن کاملAsymptotic Results with Generalized Estimating Equations for Longitudinal Data
We consider the marginal models of Liang and Zeger [Biometrika 73 (1986) 13–22] for the analysis of longitudinal data and we develop a theory of statistical inference for such models. We prove the existence , weak consistency and asymptotic normality of a sequence of estimators defined as roots of pseudo-likelihood equations. 1. Introduction. Longitudinal data sets arise in biostatistics and li...
متن کاملMultiscale Adaptive Regression Models for Neuroimaging Data.
Neuroimaging studies aim to analyze imaging data with complex spatial patterns in a large number of locations (called voxels) on a two-dimensional (2D) surface or in a 3D volume. Conventional analyses of imaging data include two sequential steps: spatially smoothing imaging data and then independently fitting a statistical model at each voxel. However, conventional analyses suffer from the same...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: NeuroImage
سال: 2013
ISSN: 1053-8119
DOI: 10.1016/j.neuroimage.2013.01.034