The oceans play a pivotal role in drawing down atmospheric carbon dioxide (CO₂) and have so far acted as a brake on the full ...

Covariance is a statistical measure of how two assets move in relation to each other. It provides diversification and reduces the overall volatility for a portfolio. A positive covariance ...

Dobriban, Edgar 2015. Efficient computation of limit spectra of sample covariance matrices. Random Matrices: Theory and Applications, Vol. 04, Issue. 04, p. 1550019.

where nused is the number of non-missing observations and np is the number of estimable parameters. The standard error reported for the parameters is the sqrt of the ...

Research shows prenatal exposure to SARS-CoV-2 linked to slight regulatory behavior improvements in infants, but no ...

A study of children exposed to maternal COVID-19 before birth found no adverse neurodevelopmental outcomes by 2 years and a ...

Dillon and Goldstein (1984) provide the following formal definition of principal components analysis (PCA): Principal components analysis transforms the original set of variables into a smaller set of ...

This high-fidelity alternative to photometry is a relatively new technique for turning 2D photos or video into 3D scenes. Adobe's free new app Substance 3D Viewer supports it, and we've seen some ...

To address the secondary study aims, homogeneity assumptions were checked and thus separate multivariate analyses of covariance (MANCOVAs) for females and males were used to evaluate the relationships ...

Robert Stelzer, MathSciNet 'This book deals with the analysis of covariance matrices under two different assumptions: large-sample theory and high-dimensional-data theory. While the former approach is ...