AbstractWe present a parameterization of the class PD(n) of positive definite n×n matrices using regular vines and partial correlations. Using a bijection from (−1,1)n2→C(n) (C(n) is the class of n×n correlation matrices) with a clear probabilistic interpretation [Ann. Statist. 30 (4) (2002) 1031], we suggest a new approach to various problems involving positive definiteness
Indefinite estimates of positive semidefinite matrices arise in many data analysis applications invo...
The correlational structure of a set of variables is often conveniently described by the pairwise pa...
Indefinite approximations of positive semidefinite matrices arise in many data analysis applications...
AbstractWe present a parameterization of the class PD(n) of positive definite n×n matrices using reg...
AbstractThis paper extends the results in [D. Kurowicka, R.M. Cooke, A parametrization of positive d...
AbstractThis paper extends the results in [D. Kurowicka, R.M. Cooke, A parametrization of positive d...
AbstractA d-dimensional positive definite correlation matrix R=(ρij) can be parametrized in terms of...
AbstractWe consider the question of whether a real partial positive definite matrix (in which the sp...
AbstractWe extend and improve two existing methods of generating random correlation matrices, the on...
Abstract. Indefinite estimates of positive semidefinite matrices arise in many data analysis ap-plic...
Indefinite symmetric matrices that are estimates of positive definite population matrices occur in a...
Indefinite symmetric matrices that are estimates of positive definite population matrices occur in a...
Indefinite symmetric matrices that are estimates of positive definite population matrices occur in a...
Abstract. Indefinite approximations of positive semidefinite matrices arise in many data anal-ysis a...
Indefinite estimates of positive semidefinite matrices arise in many data analysis applications invo...
Indefinite estimates of positive semidefinite matrices arise in many data analysis applications invo...
The correlational structure of a set of variables is often conveniently described by the pairwise pa...
Indefinite approximations of positive semidefinite matrices arise in many data analysis applications...
AbstractWe present a parameterization of the class PD(n) of positive definite n×n matrices using reg...
AbstractThis paper extends the results in [D. Kurowicka, R.M. Cooke, A parametrization of positive d...
AbstractThis paper extends the results in [D. Kurowicka, R.M. Cooke, A parametrization of positive d...
AbstractA d-dimensional positive definite correlation matrix R=(ρij) can be parametrized in terms of...
AbstractWe consider the question of whether a real partial positive definite matrix (in which the sp...
AbstractWe extend and improve two existing methods of generating random correlation matrices, the on...
Abstract. Indefinite estimates of positive semidefinite matrices arise in many data analysis ap-plic...
Indefinite symmetric matrices that are estimates of positive definite population matrices occur in a...
Indefinite symmetric matrices that are estimates of positive definite population matrices occur in a...
Indefinite symmetric matrices that are estimates of positive definite population matrices occur in a...
Abstract. Indefinite approximations of positive semidefinite matrices arise in many data anal-ysis a...
Indefinite estimates of positive semidefinite matrices arise in many data analysis applications invo...
Indefinite estimates of positive semidefinite matrices arise in many data analysis applications invo...
The correlational structure of a set of variables is often conveniently described by the pairwise pa...
Indefinite approximations of positive semidefinite matrices arise in many data analysis applications...