data) one wants to find the "nearest" correlation matrix to an "approximate" correlation matrix (e.g., a matrix which typically lacks semi-definite positiveness...

The cross-correlation matrix of two random vectors is a matrix containing as elements the cross-correlations of all pairs of elements of the random vectors...

Autocorrelation, sometimes known as serial correlation in the discrete time case, measures the correlation of a signal with a delayed copy of itself....

entries of X {\displaystyle \mathbf {X} } itself, those forming the correlation matrix of X {\displaystyle \mathbf {X} } . If each of X {\displaystyle \mathbf...

covariance matrix (also known as auto-covariance matrix, dispersion matrix, variance matrix, or variance–covariance matrix) is a square matrix giving the...

In statistics, the Pearson correlation coefficient (PCC) is a correlation coefficient that measures linear correlation between two sets of data. It is...

variable and the j th one. Likewise, the correlations can be placed in a correlation matrix. In the case of a time series which is stationary in the wide sense...

reduced correlation matrix reproduces the correlation matrix as nearly as possible, except for the diagonal elements of the correlation matrix which are...

into clusters based on their correlations, forming a hierarchical tree structure. Quasi-Diagonalization: The correlation matrix is reordered based on the...

spatial correlation matrix R R {\displaystyle \scriptstyle \mathbf {R} _{R}} and post-multiplied by transmit-side spatial correlation matrix R T {\displaystyle...

with n elements and Y(t) is a vector with q elements, then an n×q matrix of correlation functions is defined with i , j {\displaystyle i,j} element C i...

In linear algebra, a Toeplitz matrix or diagonal-constant matrix, named after Otto Toeplitz, is a matrix in which each descending diagonal from left to...

covariance matrix based on sampled data from X {\displaystyle X} and Y {\displaystyle Y} (i.e. from a pair of data matrices). Canonical-correlation analysis...

evaluate a confusion matrix is the Matthews correlation coefficient (MCC). Other metrics can be included in a confusion matrix, each of them having their...

elements) is the n × p {\displaystyle n\times p} matrix The correlation matrix is related to the covariance matrix by R X X = K X X + E ⁡ [ X ] E ⁡ [ X ] T {\displaystyle...

{\displaystyle y} (dependent variable), and the correlation matrix R x x {\displaystyle R_{xx}} of correlations between predictor variables. It is given by...

element is known. Exchangeable sequences have some basic covariance and correlation properties which mean that they are generally positively correlated....

"remarkable" correlations of the correlation matrix, by a solid line (positive correlation) or dotted line (negative correlation). A strong correlation is not...

using the inverse of the correlation matrix, the invariant portfolio employs the inverse of the square root of the correlation matrix. The optimization problem...

In statistics, polychoric correlation is a technique for estimating the correlation between two hypothesised normally distributed continuous latent variables...

covariance matrix or the correlation matrix (in which each variable is scaled to have its sample variance equal to one). For the covariance or correlation matrix...

of describing the confusion matrix of true and false positives and negatives by a single number, the Matthews correlation coefficient is generally regarded...

{\displaystyle \ x=s+v.\,} Let us define the auto-correlation matrix of the noise, reminding ourselves that this matrix has Hermitian symmetry, a property that...

formula to update the inverse of the correlation matrix, which may need less work. In particular, if the related matrix differs from the original one by only...

iconography of correlations, or representation of correlations, is a data visualization technique which replaces a numeric correlation matrix by its graphical...

For a given correlation matrix R ∈ [ − 1 , 1 ] d × d {\displaystyle R\in [-1,1]^{d\times d}} , the Gaussian copula with parameter matrix R {\displaystyle...

obtain a sample partial correlation). Note that only a single matrix inversion is required to give all the partial correlations between pairs of variables...

different correlation functions resulting in the same S-matrix and therefore describing the same physics. They are closely related to correlation functions...

out. Thereafter, the average squared off-diagonal correlation for the subsequent correlation matrix is computed for Step 1. On Step 2, the first two principal...

In statistics, Spearman's rank correlation coefficient or Spearman's ρ is a number ranging from -1 to 1 that indicates how strongly two sets of ranks...