Identification and Robustness with Contaminated and Corrupted Data
指出在稳健估计的误差模型下，未识别的总体参数通常可以被界定，并给出估计这些边界的方法，通过实例说明当数据可能被污染或损坏时，估计边界比点估计更自然。
Robust estimation aims at developing point estimators that are not highly sensitive to errors in the data. However, the population parameters of interest are not identified under the assumptions of robust estimation, so the rationale for point estimation is not apparent. This paper shows that under error models used in robust estimation, unidentified population parameters can often be bounded. The bounds provide information that is not available in robust estimation. For example, it is possible to obtain finite bounds on the population mean under contaminated sampling. A method for estimating the bounds is given and illustrated with an application. It is argued that when the data may be contaminated or corrupted, estimating the bounds is more natural than attempting point estimation of unidentified parameters