独立非齐次观测值的稳健有界影响检验
Robust Bounded Influence Tests for Independent Non-Homogeneous Observations
摘要：
实验经常产生非同分布的数据用于统计分析。在这种设置下的假设检验通常使用似然比检验进行，该检验在离群值和模型误设方面是非稳健的。本文考虑了独立分布的非同分布观测值的建立问题，并基于密度幂离建立了一类一般的参数假设检验的检验统计量.所提出的测试有界影响函数，是非常强大的数据污染方面，有很高的权力对连续的替代品，并在任何固定的替代品是一致的。该方法说明了简单的和广义的线性回归模型与固定的协变量。
英文摘要：
Experiments often yield non-identically distributed data for statistical analysis. Tests of hypothesis under such set-ups are generally performed using the likelihood ratio test, which is non-robust with respect to outliers and model misspecification. In this paper, we consider the set-up of non-identically but independently distributed observations and develop a general class of test statistics for testing parametric hypothesis based on the density power divergence. The proposed tests have bounded influence functions, are highly robust with respect to data contamination, have high power against contiguous alternatives, and are consistent at any fixed alternative. The methodology is illustrated by the simple and generalized linear regression models with fixed covariates.
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