Robust Bounded-Influence Tests in General Parametric Models
该文提出了基于M估计的稳健Wald检验、得分检验和似然比检验，通过限制自标准化灵敏度来稳定检验水平，并推导了最优有界影响检验，适用于一般参数模型的假设检验。
Abstract We introduce robust tests for testing hypotheses in a general parametric model. These are robust versions of the Wald, scores, and likelihood ratio tests and are based on general M estimators. Their asymptotic properties and influence functions are derived. It is shown that the stability of the level is obtained by bounding the self-standardized sensitivity of the corresponding M estimator. Furthermore, optimally bounded-influence tests are derived for the Wald- and scores-type tests. Applications to real and simulated data sets are given to illustrate the tests' performance. Key Words: Fréchet differentiabilityInfluence functionLogistic regressionM estimatorsScores testWald test