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Exact(8)
This naturally pushes up the standard errors, and in some cases is enough to make the coefficient of interest turn insignificant.
Subsequently, to make the coefficient matrix in SLE (8) possess nice property and low computational cost, we consider its optimization and modification as follows.
It is important to get statistical data for residual strength for both ultimate and limit allowables because the presence of damage will tend to make the coefficient of variation larger than what one can expect for undamaged structure.
By acting Ad ( exp ( a 4 a 1 ) V 3 ) ) Open image in new window on V ′ Open image in new window, we can make the coefficient of V 4 Open image in new window vanish, so we obtain: V ″ = a 1 V 1 + a 2 V 2. Open image in new window.
Now, we can simplify V as follows: Case (1): If a 1 ≠ 0 Open image in new window, then we act on V by Ad ( exp ( − a 3 a 1 ) V 4 ) ) Open image in new window, and hence, we can make the coefficient of V 3 Open image in new window vanish.
The proportion of explained variability in the model is measured with the coefficient of determination, R 2. As noted by Miljkovic and Miljkovic (2014), it would be ideal to be able to convert the data into natural logarithms to make the coefficient estimates reported in the form of elasticity; however, the numerous zeroes presented in economic losses prevent this transformation.
Similar(52)
Moreover, to make the coefficients conversion more reliable in the iterative process, we bring the non-local self-similarity constraint to regularize the HR sparse coefficients updates.
As before, it is useful to consider a new dimensionless variable (tilde {x}), this time chosen to make the coefficients of Eq. (32) more similar to those of the constant coefficient case.
In order to make the coefficients more manageable, they were multiplied by 25 and named "risk scores".
To make the coefficients of the models in group-ii regressions easily interpretable they have been exponentially transformed and reported as a value B. The interpretation is that a one unit (e.g. from zero to one) increase of independent variable would result in (B-1)*100 percentage change in OOPE.
Controlling for high school GPA again decreases the size of the coefficient.19 Controlling for institution again makes the coefficient smaller (i.e., less negative).
Related(20)
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