论文标题
基于自举样品增强机制的标准审查回归模型的模型选择标准
Model selection criteria of the standard censored regression model based on the bootstrap sample augmentation mechanism
论文作者
论文摘要
统计回归技术是一种非常重要的数据拟合工具，可以探索随机现象的潜在产生机制。因此，模型选择或变量选择变得非常重要，以确定对有趣响应的最佳解释效果的最合适的模型。在本文中，我们在有限的观察信息的情况下讨论并比较了标准审查回归模型（TOBIT回归模型）的基于自举的模型选择标准。蒙特卡洛数值证据表明，基于自举样品增强策略的模型选择标准的性能将比其替代方案更具竞争力，例如Akaike信息标准（AIC）和贝叶斯信息标准（BIC）等。在观察信息不足的情况下。同时，数值仿真实验进一步表明，由于数据信息的不足而引起的模型识别风险，例如高审查率和相当有限的观察结果，可以通过以自举样品增强策略来充分补偿科学计算成本。我们还将推荐的基于Bootstrap的模型选择标准应用于TOBIT回归模型，以适合真实的Fidelity数据集。
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.
