Application of copulas to improve covariance estimation for partial least squares.

ABSTRACT: Dimension reduction techniques, such as partial least squares, are useful for computing summary measures and examining relationships in complex settings. Partial least squares requires an estimate of the covariance matrix as a first step in the analysis, making this estimate critical to the results. In addition, the covariance matrix also forms the basis for other techniques in multivariate analysis, such as principal component analysis and independent component analysis. This paper has been motivated by an example from an imaging study in Alzheimer's disease where there is complete separation between Alzheimer's and control subjects for one of the imaging modalities. This separation occurs in one block of variables and does not occur with the second block of variables resulting in inaccur

SUBMITTER: D'Angelo GM 

PROVIDER: S-EPMC3543761 | biostudies-literature | 2013 Feb

REPOSITORIES: biostudies-literature