  The statistical regression technique is an extraordinarily essential data
fitting tool to explore the potential possible generation mechanism of the
random phenomenon. Therefore, the model selection or the variable selection is
becoming extremely important so as to identify the most appropriate model with
the most optimal explanation effect on the interesting response. In this paper,
we discuss and compare the bootstrap-based model selection criteria on the
standard censored regression model (Tobit regression model) under the
circumstance of limited observation information. The Monte Carlo numerical
evidence demonstrates that the performances of the model selection criteria
based on the bootstrap sample augmentation strategy will become more
competitive than their alternative ones, such as the Akaike Information
Criterion (AIC) and the Bayesian Information Criterion (BIC) etc. under the
circumstance of the inadequate observation information. Meanwhile, the
numerical simulation experiments further demonstrate that the model
identification risk due to the deficiency of the data information, such as the
high censoring rate and rather limited number of observations, can be
adequately compensated by increasing the scientific computation cost in terms
of the bootstrap sample augmentation strategies. We also apply the recommended
bootstrap-based model selection criterion on the Tobit regression model to fit
the real fidelity dataset.
