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The proposed integrated method performs better due to the more accurate estimating of the model parameters.
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These residuals, computed from the available data, are treated as estimates of the model error, ε.
The least squares method is the most widely used procedure for developing estimates of the model parameters.
For simple linear regression, the least squares estimates of the model parameters β0 and β1 are denoted b0 and b1.
Maximum likelihood estimates of the model parameters are obtained.
The cross-validation estimates of the model performance are shown in Table 3.
It tries to invert the operator ({mathbf{G}}) to get an estimate of the model.
Thus, the estimates of the model parameters must be obtained via numerical methods.
Coefficient estimates of the model are given in the Additional file 1.
Then, the estimates of the model parameters are given by, where and.
The parameter estimates of the model are asymptotically consistent and efficient.
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