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info.json
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{
"abstract": "There has been a surge of interest in developing robust estimators for models with heavy-tailed and bounded variance data in statistics and machine learning, while few works impose unbounded variance. This paper proposes two types of robust estimators, the ridge log-truncated M-estimator and the elastic net log-truncated M-estimator. The first estimator is applied to convex regressions such as quantile regression and generalized linear models, while the other one is applied to high dimensional non-convex learning problems such as regressions via deep neural networks. Simulations and real data analysis demonstrate the robustness of log-truncated estimations over standard estimations.",
"authors": [
"Lihu Xu",
"Fang Yao",
"Qiuran Yao",
"Huiming Zhang"
],
"emails": [
"lihuxu@um.edu.mo",
"fyao@math.pku.edu.cn",
"yb97478@connect.um.edu.mo",
"zhanghuiming@buaa.edu.cn"
],
"id": "22-0034",
"issue": 92,
"pages": [
1,
46
],
"title": "Non-Asymptotic Guarantees for Robust Statistical Learning under Infinite Variance Assumption",
"volume": 24,
"year": 2023
}