Robust and consistent variable selection in high-dimensional generalized linear models
针对广义线性模型中异常值等偏离假设的问题，提出一种稳健的惩罚拟似然估计量，该估计量在高维下具有Oracle性质且在模型邻域内稳定，模拟和真实数据验证了其有限样本表现。
Generalized linear models are popular for modelling a large variety of data. We consider variable selection through penalized methods by focusing on resistance issues in the presence of outlying data and other deviations from assumptions. We highlight the weaknesses of widely-used penalized M-estimators, propose a robust penalized quasilikelihood estimator, and show that it enjoys oracle properties in high dimensions and is stable in a neighbourhood of the model. We illustrate its finite-sample performance on simulated and real data.