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This is quite remarkable taking the often much larger scatter of main diagonal data into account.
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The Kappa measured the percentage of data values in the main diagonal of the table and then adjusted those values for the amount of agreements that could be expected due to chance alone.
κ measures the percentage of data values in the main diagonal of a 2 × 2 table and then adjusts these values for the amount of agreement that could be expected due to chance alone.
{mathcal C}^{N_{c} times 1}}) and ({{mathbf z}_{d} != [!z(n_{1}), cdots !,} z(n_{N_{c}})]^{T} in {mathcal C}^{N_{c} times 1}) are received coded data symbols and AWGN, respectively, X d =diag(x d ) is a diagonal matrix with the precoded data vector x d on its main diagonal, and (textbf {h}_{d} = [h(n_{1}), dots, h(n_{N_{c}})]^{T} in mathcal {C}^{N_{c} times 1}).
When the independence model is not adequate to explain the data, a component measuring the extra agreement present on the main diagonal is added to the model.
'Couple of hours'!...and on completing the puzzle in record time, notices something unusual hidden in the main diagonal.
Remove the entries below the main diagonal.
Symbol diag(a) denotes a diagonal matrix with its main diagonal entries being vector a.
diag denotes the diagonal matrix with elements on the main diagonal.
The matrix D x) is a diagonal matrix with vector x on its main diagonal.
where, are orthogonal matrices and is a diagonal matrix with singular values on its main diagonal.
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