ROBUST MODEL SELECTION AND M-ESTIMATION
ROBUST MODEL SELECTION AND M-ESTIMATION
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DOI：
10.1017/s0266466600007775
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发表时间：
1993-09-01
影响因子：
0.8
通讯作者：
MACHADO, JAF
中科院分区：
文献类型：
--
作者：
MACHADO, JAF
研究了基于目标函数定义的M-估计量的施瓦茨信息准则（SIC）的定性稳健性。定性鲁棒性的定义提供了适当的模型选择，它示出的关键限制需要实现鲁棒性模型选择的目标函数的一致有界性。在此过程中，SIC的一般M-估计的渐近性能进行了研究。本文最后用Monte Carlo方法研究了SIC在不同样本目标函数下的有限样本行为。
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.