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The difference between the two error rates provides an indication about the performance of the estimation algorithm.
A simple inequality is introduced to derive the combinations between the two error velocities that guarantee unconditionally stable feedback loops.
In addition, the correlation between the two error terms is not significant for the residents in the North-Centre of Italy and for those with a scientific education.
Writing the difference between the two error terms as ϑ ij = ε il -ε ij, the so-called random effects specification reads (Johnson and Desvousges [10]), ϑ ij = υ i + η ij.
However, separate estimation of mismatch attainment (Eqn. 1) may be subject to selection bias given the potential for correlation between the two error terms u i and v i.
Regarding the scale between the two error types, the ones provided in AIUB's solutions underpredict the estimated errors roughly by a factor of 10, a factor of 4 6 in case of ASU and the combined solution, and a factor of 3 in case of IfG, except for the last 3 months, which resembles closely ASU.
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No standard choice of r can be regarded as optimum because the balance between the two errors shifts according to the application.
This allows us to attribute the difference between the two errors to MODIS viewing-geometry artifacts and obtain an upper limit on AOT errors caused by along-track sampling.
In recent analysis of this issue, Zeger et al. (2000) demonstrated that in the case of two pollutants measured with error, the correlation between the two pollutants, the variances of measurement errors of these two pollutants, and the correlation between the two errors would predict the magnitude and direction of bias.
When the pooled DLCV error is between the two individual errors, and lower/higher than the weighted mean (number of samples chosen as weights) we label the effect as marginal synergetic/marginal anti-synergetic.
The parameter enables a tradeoff between the two possible error probabilities of voice activity detection.
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