Examples of using Covariance matrix in English and their translations into Portuguese
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This master thesis proposes a new approach for the design of optimal adaptive detectors for centralized cooperative spectrum sensing based on the eigenvalues of the received signal covariance matrix.
scaled nonlinearly, from the received signal covariance matrix and the also standardized corresponding indexes.
The standard error of the coefficients was calculated using White's covariance matrix because the model showed evidence of heteroskedasticity according to the Bartlett test p-value< 0.0000, Levene test p-value< 0.0003 and Brown-Forsythe test p-value< 0.0819.
This work proposes an optimal velocity estimator, using maximum likelihood covariance matrix, for synthetic aperture radars(sar) with circular geometry, called arcsar,
The goal of this work is to tune the background error covariance matrix while assimilating doppler radar data in order to improve the analysis
The simplest method relies on the Cholesky decomposition method of the covariance matrix(explained below), which on a grid of size n{\displaystyle n}
choice of the covariance matrix structure, definition and utility of the odds ratios
The background-errors covariance matrix is a key element in da, because it contributes
If you have a matrix X which is your time trading sets written in rows where x1 transpose down to x1 transpose, this covariance matrix sigma actually has a nice vectorizing implementation.
the posterior volatility of data cloning estimates, and access the identification problem globally through the maximum eigenvalue of the posterior data cloning covariance matrix.
one first estimates the covariance matrix of each class, usually based on samples known to belong to each class.
allowing the covariance matrix to be time- varying.
The sample covariance matrix has formula_18 in the denominator rather than formula_19 due to a variant of Bessel's correction:
Let's say we want to reduce the data to n dimensions to k dimension What we're going to do is first compute something called the covariance matrix, and the covariance matrix is commonly denoted by this Greek alphabet which is the capital Greek alphabet sigma.
It was found that in the risk parity model the form to obtain the covariance matrix has little influence on the final result,
An important step in determining the least squares coefficients is the use of a covariance matrix that presents a low fitness value,
then the covariance matrix Σ is the matrix whose("i","j")
The sample covariance matrix has N- 1{\displaystyle\textstyle N-1} in the denominator rather than N{\displaystyle\textstyle N}
The sample covariance matrix is a square matrix whose i,
is a single number(the arithmetic average of the observed values of that variable) and the sample covariance matrix is also simply a single value the sample variance of the observed values of that variable.