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Second, models are estimated with neural network learning algorithms and the estimation of the models benefits from algorithm cooperation weight decay and early stopping.
The estimation of the models parameter was made using least square method.
As the features used in all five methods are based on statistical models, more data leads to better estimation of the models.
The validation and comparison process is performed through statistical means to examine how well the estimation of the models fits the reality.
In this section, we use the parameters obtained from the estimation of the models to simulate the labor force participation rate and the employment rate under three different taxation systems that have been considered in the political and academic debate.
Three measures of reliability are explored and incorporated in the estimation of the models: standard deviation (a classical measure in the research literature); shortened right range (typically found in departure time choice models); and interquartile range (75th 25th percentile).
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The main challenge of this type of modeling lays in the estimation of the model parameters which may not be directly measurable from clinical investigations.
Once a tentative model is identified, the estimation of the model parameters is straightforward.
Here, we focus particularly on the development of techniques for the estimation of the model order.
For the estimation of the model parameter B see the Section 3.3.
The estimation of the model parameters allows for the design of power control algorithms.
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