The comparison study of the model selection criteria on the Tobit regression model based on the bootstrap sample augmentation mechanisms

The statistical regression technique is an essential data fitting tool to explore the generation mechanism of the random phenomenon. Therefore, the model selection technique is becoming important. Meanwhile, bootstrap-based sample augmentation mechanisms are becoming indispensable when the reliable statistical inference of the model selection is expected to be made when the sample size is unsufficient. In this paper, the model selection performance of the bootstrap-based model selection criteria on the Tobit regression model are compared through the intensive Monte Carlo simulation experimentation. The simulation experiment demonstrates that the model identification risk of the recommended bootstrap-based model selection criteria can be adequately compensated by increasing the scientific computation cost in terms of the different bootstrap sample augmentation mechanisms. The recommended bootstrap-based model selection criterion is applied to fit the fidelity dataset.