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To model the distribution of the errors correctly for the combined bundle adjustment of oblique images, this paper proposes an asymmetric re-weighting method.
Despite their seemingly different problem formulation, it is shown that both methods can be reduced to the same mathematical kernel problem, i.e. finding the closest (in a certain sense) weighted low rank matrix approximation where the weight is derived from the distribution of the errors in the data.
The skill of the NWP models is quantified: (1) by visual examination of the distribution of the errors in storm total rainfall for the different lead-times, and numerical examination of the first three moments of the error distribution; (2) relative to climatology at the daily scale.
An important fact about the distribution of the errors is shown in Table 3.
In Section 5, the solutions are compared individually to investigate the distribution of the errors.
The normal probability plot is used for evaluating the assumption that the distribution of the errors (residuals) is normal.
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The control performance of the system with respect to the frequency, the distribution of the error sensors and the control sources are discussed.
Figures 3(c) and 3 d) show the distribution of the error and u.
Applying the SZ method, λ i is unknown because the distribution of the error terms is not specified.
The econometric specification depends on the hypotheses made with regard to the distribution of the error term of the probability of Eq. (4).
Since we have completely specified the distribution of the error terms given the regressors, the model can be estimated with maximum likelihood.
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