Optimal selection of model order for a class of nonlinear systems using the bootstrap

Nonlinear system identification involves selecting the order of the given model based on the input-output data. A bootstrap model selection procedure which selects the model by minimising bootstrap estimates of the prediction error is developed. Bootstrap based model selection procedures are attractive because the bootstrap observations generated for the model selection can also be used in subsequent inference procedures. The proposed method is simple and computationally efficient.