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I replace both variances in the denominator with the covariance of sibling composite errors, as explained above.
I replace the numerator with the covariance of sibling composite errors for different outcomes, i.e., (text {Cov} left (varepsilon _{ifst}^{k}, varepsilon _{i'fs't}^{ell } right)) for i≠i ′.
Note that ρ 1 in Eq. (3) will not be equal to ρ 1 in Eq. (5) as composite errors differ except for (x_{i}=hat {x}_{i}).
The variance of the family innovation is equal to the covariance of the composite errors as long as two conditions hold.
With the composite errors separating method (JLMS for short) advanced by Jondrow et al. [5], the technical inefficiency u iτ is separated from the composite error ν iτ − u iτ as, T{E}_{itau}= exp left -{u}_{itau}right) (9).
Comparisons between our results and those by existing mathematical models show that the composite errors are acceptable, with an average difference of 14.5% for dehumidifiers and 6.83% for regenerators.
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This illustrates the goodness of the composite error's distributional assumptions.
The update law with composite error feedback improves uncertainty approximation accuracy and trajectory tracking accuracy.
Hence the influential composite error deviations take the form (sum_{i}Delta B_{i}) and (sum_{i}Delta J_{i}).
We specify composite error terms in the two equations, where u represents an individual fixed effect, possibly correlated with the explanatory variables, and ε is white noise.
The proposed method enables escape through undesired sub-optimal solutions on the composite error surface by means of dynamic tunneling.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com