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A Bayesian decision-theoretic method identified those morphological features, which best explained neuropsychological test scores in the context of a multivariate response linear model with interactions.
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Tests were made for associations within each functional domain by exploring a linear multivariate response model.
The goal of PCA is to identify a few uncorrelated linear combinations of original data (i.e., principal components) that capture as much multivariate response variation as possible.
The integral (multivariate) response to Cu exposure (defined as the response of all biochemical measurements at the same time) was explored by multivariate analysis using principal component analysis (PCA) and linear discriminant analysis (LDA).
Methods for multivariate responses.
We consider a multivariate linear response regression in which the number of responses and predictors is large and comparable with the number of observations, and the rank of the matrix of regression coefficients is assumed to be small.
(Multivariate linear path models involve linear mean function specifications).
The purpose of this paper is to discuss response surface designs for multivariate generalized linear models (GLMs).
The critical resolution, analysis time, and peak width were responses modeled by multivariate linear regression.
To understand the changes in male acoustic signals in response to the broadcast stimulus, I developed a multivariate general linear model (GLM) for five acoustic characters.
To jointly model many brain voxels' responses to designed stimuli, we present a new low-rank multivariate general linear model (LRMGLM) for stimulus-evoked fMRI data.
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