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Hence, the well-testing analysis is an inverse solution; in respect of the way, many problems are still associated with the model selection, such as uncertain data and non-uniqueness of the pressure responses generated by different reservoir models.

No model selection such as backwards elimination was performed.

Improvements to statistical approaches for model selection, such as model averaging, should be pursued.

Ideally, objective criteria for model selection, such as the likelihood ratio tests easily implemented in MODELTEST [ 26] should be used before calculating a rate.

Aside from their usefulness in model selection, such predictive differences could also be attributed to the different mechanisms present in the different models.

Model identification/selection metric: the use of more advanced metrics for model selection such as the likelihood ratio or the F-test can not be used in this approach since they rely on pair-wise comparisons.

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Similar(53)

Subspace approach eliminates the intermediate step of model structure selection such as model order and time-delay determination or truncation of the step response model in the identification stage, which simplifies the design procedure of the predictive controllers.

The strength of the penalty can be chosen either to select a good predictive model (via methods such as computationally expensive cross validation), through maximum likelihood-based model selection criterion (such as the BIC), or to select a model that controls for type I error, as done here.

We could include all variables in our model and select the best-fitting model via model selection criteria such as the Akaike Information Criterion (AIC), which is a statistical tool to assess the quality of a statistical model.

Therefore, we select the ML poset without an additional model selection criterion such as the Akaike or Bayesian information criterion (AIC and BIC, respectively).

We used model selection criteria such as AIC and BIC as well as backward selection to select the most favored models.

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