【摘要】 指数样本中多个异常值的非一致性检验因受masking或swamping效应的影响而变得十分的困难和复杂，解决这一问题的关键在于K值的确定，传统的方法是无能为力的．本文基于变量选择的AIC准则的思想提出了异常值检验的一种新方法，它具有不预先指定k，计算简单且通过达到极大化MAIC就能达到确定k和消除检验中的masking或swamping的优点．还给出了易计算检验显著水平的统计量和公式．最后，通过实例的验证标明本文方法的有效性．更多还原

【Abstract】 The discordancy test for many outliers in an exponential sample is very difficult and complicated because of the masking or swamping effect. The key to solve the question lies in the determination of k, the nunlber of contaminants that are discordant outliers in an exponential sample. The avaiable outlier test metbods often do not succeed in solving this problem. In this paper, a methods based on idea of Akaike’s information criterion of choosing variables for the detection of outliers is proposed, which does not require presetting k and highly complicated compution, can accurately detect k by maximizing statistic called modified Akaike’s in formation criterion (MAIC). The statistic and formula calculated easily the significance level of test is also proposed. Finally, numerical examples are given, showing our method is very useful in practice.更多还原