Robust Model Selection and M-Estimation
研究了基于M估计目标函数的Schwarz信息准则的定性稳健性，提出模型选择中稳健性的定义，并证明目标函数一致有界是实现稳健性的关键。通过蒙特卡洛模拟考察了有限样本表现。
This paper studies the qualitative robustness properties of the Schwarz information criterion (SIC) based on objective functions defining M -estimators. A definition of qualitative robustness appropriate for model selection is provided and it is shown that the crucial restriction needed to achieve robustness in model selection is the uniform boundedness of the objective function. In the process, the asymptotic performance of the SIC for general M -estimators is also studied. The paper concludes with a Monte Carlo study of the finite sample behavior of the SIC for different specifications of the sample objective function.