Introduction Each year, millions of people experience recurrent diverticulitis episodes. Elective sigmoid colon resection reduces the risk of recurrence, but The American Society of Colon and Rectal ...
Objectives to describe the evolution of anxiety during the COVID-19 pandemic in France and to assess whether it differed according to pre-existing alcohol misuse. Design A prospective longitudinal ...
Background The National Heart Failure Audit gathers data on patients coded at discharge (or death) as having heart failure as ...
In the first case, there is a strong upward-sloping relationship between X and Y; in the second case, no apparent relationship; in the third case, a strong downward-sloping relationship. Note the ...
Abstract: Linear estimation of signals is often based on covariance matrices estimated from training, which can perform poorly if the training data are limited and the estimated covariance matrices ...
It would be nice to have an example that shows how to use consider parameters in an estimation/covariance analysis. This could either extend an already existing example (e.g. the covariance ...
This important study shows a surprising scale-invariance of the covariance spectrum of large-scale recordings in the zebrafish brain in vivo. A convincing analysis demonstrates that a Euclidean random ...
Abstract: Interferometric phase linking (IPL) has become a prominent technique for processing images of areas containing distributed scatterers in SAR interferometry. Traditionally, IPL consists in ...
MANOVA is a statistical test that extends the scope of the more commonly used ANOVA, that allows differences between three or more independent groups of explanatory (independent or predictor) ...
Leslie Kramer is a writer for Institutional Investor, correspondent for CNBC, journalist for Investopedia, and managing editor for Markets Group. Correlation measures the linear relationship between ...
The estimation of covariance matrices is very important in many fields, such as statistics. In real applications, data are frequently influenced by high dimensions and noise. However, most relevant ...
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