Generalized reduced rank latent factor regression for high dimensional tensor fields, and neuroimaging-genetic applications.

ABSTRACT: We propose a generalized reduced rank latent factor regression model (GRRLF) for the analysis of tensor field responses and high dimensional covariates. The model is motivated by the need from imaging-genetic studies to identify genetic variants that are associated with brain imaging phenotypes, often in the form of high dimensional tensor fields. GRRLF identifies from the structure in the data the effective dimensionality of the data, and then jointly performs dimension reduction of the covariates, dynamic identification of latent factors, and nonparametric estimation of both covariate and latent response fields. After accounting for the latent and covariate effects, GRLLF performs a nonparametric test on the remaining factor of interest. GRRLF provides a better factorization of the signa

SUBMITTER: Tao C 

PROVIDER: S-EPMC5798650 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature