An Exponential-Polynomial Divergence-based Robust Information Criterion for Linear Panel Data Models and Neural Networks
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用于线性面板模型与神经网络的指数多项式散度稳健信息准则
- 研究问题
- 传统信息准则易受异常值、污染和模型误设影响时，如何构造仍能权衡拟合与复杂度、同时具有稳健性的模型选择准则？
- 方法与思路
- 作者基于指数多项式散度构建信息准则，用影响函数分析稳健性，并以广义得分匹配选择调参值，在面板和神经网络应用中评估。
- 材料陈述的贡献
- 摘要称建立理论性质和异常值敏感性控制，模拟及应用显示较似然和密度幂散度准则更稳定可靠，并提出数据驱动调参策略。
- 阅读关注点
- 核对散度、复杂度修正与目标风险的关系，以及影响函数界限、调参机制和污染设计；检查面板应用中的相关结构处理。
- 阅读边界
- 这是 arXiv 预印本，同行评审和期刊接收均未核实；摘要未给出准则公式和理论条件，所述稳定性改善不能替代具体选模误差评估。
原文摘要
Model selection is a cornerstone of statistical inference, where information criteria are widely employed to balance model fit and complexity. However, classical likelihood-based criteria are often highly sensitive to contamination, outliers, and model misspecification. In this paper, we develop a robust alternative based on the Exponential-Polynomial Divergence, a flexible extension of existing divergence measures that enhances adaptability to diverse data irregularities. The proposed Exponential-Polynomial Divergence Information Criterion preserves the objective of approximating the discrepancy between the true model and candidate models while incorporating robustness against anomalous observations. Its theoretical properties are established, and robustness is examined through influence function analysis, demonstrating controlled sensitivity to extreme data points. For practical implementation, a data-driven tuning parameter selection strategy based on generalized score matching is employed, ensuring improved computational stability and efficiency. The effectiveness of the proposed method is demonstrated through extensive simulation studies under varying contamination levels, as well as real data applications involving linear mixed-effects panel data models and neural network-based prediction tasks. The results consistently show improved stability and reliability compared to classical likelihood and density power divergence-based information criteria. The proposed framework thus provides a practical and unified approach for model selection in complex and contaminated data settings.