Random orthogonal matrix simulation with exact means, covariances, and multivariate skewness

2017 ◽  
Vol 263 (2) ◽  
pp. 510-523
Author(s):  
Michael Hanke ◽  
Spiridon Penev ◽  
Wolfgang Schief ◽  
Alex Weissensteiner
Keyword(s):  
Orthogonal Matrix ◽  
Multivariate Skewness

scholarly journals Some properties of the unified skew-normal distribution

2021 ◽  
Author(s):  
Reinaldo B. Arellano-Valle ◽  
Adelchi Azzalini
Keyword(s):  
Normal Distribution ◽  
Fourth Order ◽  
Probability Distributions ◽  
Skew Normal Distribution ◽  
Present Contribution ◽  
The Family ◽  
Multivariate Skewness ◽  
Unified Skew Normal Distribution ◽  
Skewness And Kurtosis ◽  
Skew Normal

AbstractFor the family of multivariate probability distributions variously denoted as unified skew-normal, closed skew-normal and other names, a number of properties are already known, but many others are not, even some basic ones. The present contribution aims at filling some of the missing gaps. Specifically, the moments up to the fourth order are obtained, and from here the expressions of the Mardia’s measures of multivariate skewness and kurtosis. Other results concern the property of log-concavity of the distribution, closure with respect to conditioning on intervals, and a possible alternative parameterization.

scholarly journals Relative asymptotics for orthogonal matrix polynomials

2012 ◽  
Vol 437 (7) ◽  
pp. 1458-1481 ◽  
Author(s):  
A. Branquinho ◽  
F. Marcellán ◽  
A. Mendes
Keyword(s):  
Orthogonal Matrix ◽  
Matrix Polynomials ◽  
Orthogonal Matrix Polynomials

scholarly journals Orthogonal matrix factorization enables integrative analysis of multiple RNA binding proteins

2016 ◽  
Vol 32 (10) ◽  
pp. 1527-1535 ◽  
Author(s):  
Martin Stražar ◽  
Marinka Žitnik ◽  
Blaž Zupan ◽  
Jernej Ule ◽  
Tomaž Curk
Keyword(s):  
Matrix Factorization ◽  
Binding Proteins ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Orthogonal Matrix ◽  
Integrative Analysis

scholarly journals ROTATION MATRIX SAMPLING SCHEME FOR MULTIDIMENSIONAL PROBABILITY DISTRIBUTION TRANSFER

2016 ◽  
Vol III-7 ◽  
pp. 103-110
Author(s):  
P. Srestasathiern ◽  
S. Lawawirojwong ◽  
R. Suwantong ◽  
P Phuthong
Keyword(s):  
Probability Distribution ◽  
Orthogonal Matrix ◽  
Rotation Matrix ◽  
Sampling Scheme ◽  
Uniform Rotation ◽  
Householder Transformation ◽  
Orthogonal Matrices ◽  
Matrix Sampling ◽  
Paper Address ◽  
Multidimensional Probability

This paper address the problem of rotation matrix sampling used for multidimensional probability distribution transfer. The distribution transfer has many applications in remote sensing and image processing such as color adjustment for image mosaicing, image classification, and change detection. The sampling begins with generating a set of random orthogonal matrix samples by Householder transformation technique. The advantage of using the Householder transformation for generating the set of orthogonal matrices is the uniform distribution of the orthogonal matrix samples. The obtained orthogonal matrices are then converted to proper rotation matrices. The performance of using the proposed rotation matrix sampling scheme was tested against the uniform rotation angle sampling. The applications of the proposed method were also demonstrated using two applications i.e., image to image probability distribution transfer and data Gaussianization.

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