The eddy covariance (also known as eddy correlation and eddy flux) is a key atmospheric measurement technique to measure and calculate vertical turbulent...

eigenvectors of the data's covariance matrix. Thus, the principal components are often computed by eigendecomposition of the data covariance matrix or singular...

the covariance of two variables and the product of their standard deviations; thus, it is essentially a normalized measurement of the covariance, such...

average value. It is the second central moment of a distribution, and the covariance of the random variable with itself, and it is often represented by σ 2...

function of the discrepancy between the observed covariance matrix and the model-implied covariance matrix. Chi-square increases with sample size only...

qui résultent de l'emploie du signe et sur > ou <, et sur les moyennes entre plusieurs quantités", Cours d'Analyse, 1er Partie: Analyse Algébrique 1821;...

extracting effect sizes and variance measures from various studies. Meta-analyses are integral in supporting research grant proposals, shaping treatment...

{\displaystyle \mathrm {E} (e_{t}e_{t}')=\Omega \,} . The contemporaneous covariance matrix of error terms is a k × k positive-semidefinite matrix denoted...

two matrices (X and Y), i.e. a latent variable approach to modeling the covariance structures in these two spaces. A PLS model will try to find the multidimensional...

that the class covariances are identical, so Σ 0 = Σ 1 = Σ {\displaystyle \Sigma _{0}=\Sigma _{1}=\Sigma } ) and that the covariances have full rank....

record, the impact of mutations on shape, developmental changes in form, covariances between ecological factors and shape, as well for estimating quantitative-genetic...

more parsimonious models based on the eigenvalue decomposition of the covariance matrices, that provide a balance between overfitting and fidelity to the...

defined using the covariance matrix of the coordinates of the points. The trace, the determinant, and the largest eigenvalue of the covariance matrix can be...

corresponded frequently, has led to numerous historical analyses of their interaction. The analyses came to be called a priority dispute. The events of interest...

to the spectrum of eigenvalues in a singular value decomposition of a covariance matrix, and not directly to a frequency domain decomposition. The origins...

generally covariant equations, because ... The postulate for general covariance of the equations embraces the principle of equivalence as a special case...

{wX} _{2}\right\|^{2}}}} The solution is given by computing the two covariance matrices: R 1 = X 1 X 1 T t 1 {\displaystyle \mathbf {R} _{1}={\frac {\mathbf...

distribution with a known covariance matrix. Kost's method extends Brown's to allow one to combine p-values when the covariance matrix is known only up...

'general covariance' was used in the early formulation of general relativity, but the principle is now often referred to as 'diffeomorphism covariance'. Diffeomorphism...

for exploring spatial dependence include: spatial correlation, spatial covariance functions and semivariograms. Methods for spatial interpolation include...

incorrect. This was corrected by Poincaré (1905) who obtained full Lorentz covariance of the electrodynamic equations. Criticizing Lorentz's 1904 theory, Abraham...

obtain the estimate of the covariance matrix, the forward-only Capon uses only the forward data vectors to estimate the covariance matrix. Capon can yield...

the concept of general covariance and discovered that the hole argument was flawed. When Einstein realized that general covariance was tenable, he quickly...

with mean vector μ {\displaystyle \mu } and rank k {\displaystyle k} covariance matrix C {\displaystyle C} , then X = ( Y − μ ) T C − 1 ( Y − μ ) {\displaystyle...

research by Altonji has addressed small-sample bias in GMM estimation of covariance structures (with Lewis Segal), cross section and panel data estimators...

separate univariate t-tests to test hypotheses, as these would neglect the covariance among measures and inflate the chance of falsely rejecting at least one...

Alpha beta filter Alternative hypothesis Analyse-it – software Analysis of categorical data Analysis of covariance Analysis of molecular variance Analysis...

used estimators include sample mean, unbiased sample variance and sample covariance. A random variable that is a function of the random sample and of the...

best represent the pattern of covariance across the sets. The linear combinations will optimally describe the covariances and provide a low-dimensional...

quadratic forms in a normal system, with applications to the analysis of covariance". Math. Proc. Camb. Philos. Soc. 30 (2): 178–191. Bibcode:1934PCPS...30...