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Probability distribution of eigenspectra and eigendirections of a twodimensional, symmetric rank two random tensor

Xu, Peiliang / Grafarend, Erik

Abstract Let there be given a twodimensional symmetric rank two tensor of random type (examples:strain, stress) which is either directly observed or indirectly estimated from observations by an adjustment procedure. Under the assumption of normalityof tensor components we compute the joint probability density functionas well as the marginal probability density functionsof its eigenspectra (eigenvalues) and eigendirections (orientation parameters). Due to the nonlinearity of the relation between eigenspectra-eigendirections and the random tensor components, via the “inverse nonlinear error propagation”biases and aliases of their first and centralized second moments (mean value, variance-covariance) are expressed in terms of Jacobianand Hessianmatrices. The joint probability density function and the first and second moments thus form the fundamental of hypothesis testing and qualify control of eigenspectra (eigenvalues, principal components) and eigendirections (orientation parameters, eigenvectors, principial direction) of a twodimensional, symmetric rank two random tensor.

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Probability distribution of eigenspectra and eigendirections of a twodimensional, symmetric rank two random tensor

Xu, Peiliang / Grafarend, Erik

Abstract Let there be given a twodimensional symmetric rank two tensor of random type (examples:strain, stress) which is either directly observed or indirectly estimated from observations by an adjustment procedure. Under the assumption of normalityof tensor components we compute the joint probability density functionas well as the marginal probability density functionsof its eigenspectra (eigenvalues) and eigendirections (orientation parameters). Due to the nonlinearity of the relation between eigenspectra-eigendirections and the random tensor components, via the “inverse nonlinear error propagation”biases and aliases of their first and centralized second moments (mean value, variance-covariance) are expressed in terms of Jacobianand Hessianmatrices. The joint probability density function and the first and second moments thus form the fundamental of hypothesis testing and qualify control of eigenspectra (eigenvalues, principal components) and eigendirections (orientation parameters, eigenvectors, principial direction) of a twodimensional, symmetric rank two random tensor.

Probability distribution of eigenspectra and eigendirections of a twodimensional, symmetric rank two random tensor

Xu, Peiliang / Grafarend, Erik

Abstract Let there be given a twodimensional symmetric rank two tensor of random type (examples:strain, stress) which is either directly observed or indirectly estimated from observations by an adjustment procedure. Under the assumption of normalityof tensor components we compute the joint probability density functionas well as the marginal probability density functionsof its eigenspectra (eigenvalues) and eigendirections (orientation parameters). Due to the nonlinearity of the relation between eigenspectra-eigendirections and the random tensor components, via the “inverse nonlinear error propagation”biases and aliases of their first and centralized second moments (mean value, variance-covariance) are expressed in terms of Jacobianand Hessianmatrices. The joint probability density function and the first and second moments thus form the fundamental of hypothesis testing and qualify control of eigenspectra (eigenvalues, principal components) and eigendirections (orientation parameters, eigenvectors, principial direction) of a twodimensional, symmetric rank two random tensor.

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Title:

Probability distribution of eigenspectra and eigendirections of a twodimensional, symmetric rank two random tensor

Contributors:

Xu, Peiliang (author) / Grafarend, Erik (author)

Published in:

Journal of Geodesy ; 70

Publication date:

1996

ISSN:

DOI:

https://doi.org/10.1007/BF01090817

Type of media:

Article (Journal)

Type of material:

Print

Language:

English

Keywords:

Geodäsie , Geodynamik

Classification:

BKL: 38.73 Geodäsie

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