Conditional Robustness in Location Estimation
研究了在对称密度假设下，从条件推断角度对位置参数进行稳健点估计，提出了估计量和样本的条件稳健性度量，并应用于实际数据集。
Let Y1,…, Yn denote independent real-valued observations, each distributed according to a density p(y − θ ), where θ is an unknown parameter and p is a symmetric density function. This paper considers robust point estimation of θ from the point of view of conditional inference. Specific measures of the conditional robustness of a location estimator are introduced, as well as measures of the conditional robustness of a particular sample. Location-scale models are also considered. The results are applied to several data sets.