In our paper,we focus on robust variable selection for missing data and measurement error.Missing data and measurement errors can lead to confusing data distribution.We propose an exponential loss function with tuning parameter to apply to Missing and measurement errors data.By adjusting the parameter,the loss functioncan be better and more robust under various different data distributions.We use inverse probability weighting and additivityerrormodels to address missing data and measurement errors.Also,we find that the Atan punishment method works better.We used Monte Carlo simulations to assess the validity of robust variable selection and validated our findings with the breast cancer dataset

翻译：本文聚焦于缺失数据与测量误差场景下的稳健变量选择问题。缺失数据与测量误差可能导致数据分布失真。我们提出一种带调节参数的指数损失函数，适用于存在缺失与测量误差的数据。通过调整参数，该损失函数能在多种不同数据分布下表现更优且更具稳健性。我们采用逆概率加权与加性误差模型分别处理缺失数据与测量误差问题，并发现Atan惩罚方法具有更佳效果。通过蒙特卡洛模拟评估了稳健变量选择的有效性，并利用乳腺癌数据集验证了研究结论。