Results 1  10
of
2,958,798
and Common Principal Components
"... We propose rankbased estimators of principal components, both in the onesample and, under the assumption of common principal components, in the msample cases. Those estimators are obtained via a rankbased version of Le Cam’s onestep method, combined with an estimation of crossinformation quanti ..."
Abstract
 Add to MetaCart
We propose rankbased estimators of principal components, both in the onesample and, under the assumption of common principal components, in the msample cases. Those estimators are obtained via a rankbased version of Le Cam’s onestep method, combined with an estimation of cross
PseudoGaussian tests for common principal components
, 2008
"... The socalled Common Principal Components (CPC) Model, in which the covariance matrices Σi of m populations are assumed to have identical eigenvectors, was introduced by Flury (1984). While Gaussian parametric inference methods (Gaussian maximum likelihood estimation; Gaussian likelihood ratio testi ..."
Abstract

Cited by 4 (4 self)
 Add to MetaCart
The socalled Common Principal Components (CPC) Model, in which the covariance matrices Σi of m populations are assumed to have identical eigenvectors, was introduced by Flury (1984). While Gaussian parametric inference methods (Gaussian maximum likelihood estimation; Gaussian likelihood ratio
in Heterokurtic Elliptical Common Principal Components Models
"... The socalled Common Principal Components (CPC) Model, in which the covariance matrices Σi of m populations are assumed to have identical eigenvectors, was introduced by Flury (1984), who develops Gaussian parametric inference methods for this model (Gaussian maximum likelihood estimation and Gaussi ..."
Abstract
 Add to MetaCart
The socalled Common Principal Components (CPC) Model, in which the covariance matrices Σi of m populations are assumed to have identical eigenvectors, was introduced by Flury (1984), who develops Gaussian parametric inference methods for this model (Gaussian maximum likelihood estimation
Optimal rankbased tests for common principal components
 Bernoulli
, 2013
"... This paper provides optimal testing procedures for the msample null hypothesis of Common Principal Components (CPC) under possibly non Gaussian and heterogenous elliptical densities. We first establish, under very mild assumptions that do not require finite moments of order four, the local asympto ..."
Abstract

Cited by 2 (1 self)
 Add to MetaCart
This paper provides optimal testing procedures for the msample null hypothesis of Common Principal Components (CPC) under possibly non Gaussian and heterogenous elliptical densities. We first establish, under very mild assumptions that do not require finite moments of order four, the local
Printed in Great Britain Two generalizations of the common principal component model
"... Under the common principal component model the covariance matrices ^ , of k populations are assumed to have identical eigenvectors, that is, the same orthogonal matrix diagonalizes all ^ , simultaneously. This paper modifies the common principal component model by assuming that only q out of p eigen ..."
Abstract
 Add to MetaCart
Under the common principal component model the covariance matrices ^ , of k populations are assumed to have identical eigenvectors, that is, the same orthogonal matrix diagonalizes all ^ , simultaneously. This paper modifies the common principal component model by assuming that only q out of p
Robust tests for the common principal components model
"... In multivariate analysis we often deal with situations involving several populations, such as discriminant analysis, where the assumption of equality of scatter matrices is usually assumed. Yet sometimes, this assumption is not adequate but problems related to an excessive number of parameters wil ..."
Abstract

Cited by 4 (0 self)
 Add to MetaCart
In multivariate analysis we often deal with situations involving several populations, such as discriminant analysis, where the assumption of equality of scatter matrices is usually assumed. Yet sometimes, this assumption is not adequate but problems related to an excessive number of parameters will arise if we estimate the scatter matrices separately for each population. In many
Comparing covariance matrices: random skewers method compared to the common principal components
, 2007
"... Comparisons of covariance patterns are becoming more common as interest in the evolution of relationships between traits and in the evolutionary phenotypic diversification of clades have grown. We present parallel analyses of covariance matrix similarity for cranial traits in 14 New World Monkey ge ..."
Abstract

Cited by 6 (0 self)
 Add to MetaCart
genera using the Random Skewers (RS), Tstatistics, and Common Principal Components (CPC) approaches. We find that the CPC approach is very powerful in that with adequate sample sizes, it can be used to detect significant differences in matrix structure, even between matrices that are virtually identical
Drift Compensation of Gas Sensor Array Data by Common Principal Component Analysis
"... A new drift compensation method based on Common Principal Component Analysis (CPCA) is proposed. The drift variance in data is found as the principal components computed by CPCA. This method finds components that are common for all gasses in feature space. The method is compared in classification ta ..."
Abstract
 Add to MetaCart
A new drift compensation method based on Common Principal Component Analysis (CPCA) is proposed. The drift variance in data is found as the principal components computed by CPCA. This method finds components that are common for all gasses in feature space. The method is compared in classification
Printed in Great Britain Discriminant analysis with common principal components
"... Zhu & Hastie (2003) presented a general criterion for finding discriminant directions. To optimise their criterion, iterative methods are needed unless each class has a Gaussian distribution with a common covariance matrix. In this short paper, we present a slightly more general case where itera ..."
Abstract
 Add to MetaCart
Zhu & Hastie (2003) presented a general criterion for finding discriminant directions. To optimise their criterion, iterative methods are needed unless each class has a Gaussian distribution with a common covariance matrix. In this short paper, we present a slightly more general case where
Results 1  10
of
2,958,798