One important practical application of principal component analysis is to reduce a large number of variables, say p, to a smaller number, m, by making use of the first m principal components. This technique can easily be extended to two or more groups if the subspaces spanned by the first m principal components are the same for all groups. In this paper we develop an approximate procedure for testing such a hypothesis of common subspaces when two groups are involved. The adequacy of the approximation is investigated by a simulation and the method is illustrated by a numerical example
The common principal components (CPC) and the proportional principal components (PPC) models are two...
We study the distributed computing setting in which there are multiple servers, each holding a set o...
International audienceIn a recent paper on sensitivity of subspaces spanned by principal components,...
One important practical application of principal component analysis is to reduce a large number of v...
We consider the principal components analysis of g groups of m variables for those situations in whi...
An approximate test, based on sample eigenprojections, is obtained for testing the hypothesis that t...
Data reduction, latent root, latent vector, principal component, conditional Haar distribution,
The standard common principal components (CPCs) may not always be useful for simultaneous dimensiona...
The focus of this thesis is the common principal component (CPC) model, the generalization of princi...
This thesis consists of four papers, all exploring some aspect of common principal component analysi...
We consider tests of the null hypothesis that g covariance matrices have a partial common principal ...
We consider tests of the null hypothesis that g covariance matrices have a partial common principal ...
The aim of this paper is to propose an extension of Principal Component Analysis onto a Reference Su...
In recent years, subspace arrangements have become an increasingly popular class of mathematical obj...
Although it is simple to determine whether multivariate group differences are statistically signific...
The common principal components (CPC) and the proportional principal components (PPC) models are two...
We study the distributed computing setting in which there are multiple servers, each holding a set o...
International audienceIn a recent paper on sensitivity of subspaces spanned by principal components,...
One important practical application of principal component analysis is to reduce a large number of v...
We consider the principal components analysis of g groups of m variables for those situations in whi...
An approximate test, based on sample eigenprojections, is obtained for testing the hypothesis that t...
Data reduction, latent root, latent vector, principal component, conditional Haar distribution,
The standard common principal components (CPCs) may not always be useful for simultaneous dimensiona...
The focus of this thesis is the common principal component (CPC) model, the generalization of princi...
This thesis consists of four papers, all exploring some aspect of common principal component analysi...
We consider tests of the null hypothesis that g covariance matrices have a partial common principal ...
We consider tests of the null hypothesis that g covariance matrices have a partial common principal ...
The aim of this paper is to propose an extension of Principal Component Analysis onto a Reference Su...
In recent years, subspace arrangements have become an increasingly popular class of mathematical obj...
Although it is simple to determine whether multivariate group differences are statistically signific...
The common principal components (CPC) and the proportional principal components (PPC) models are two...
We study the distributed computing setting in which there are multiple servers, each holding a set o...
International audienceIn a recent paper on sensitivity of subspaces spanned by principal components,...