Doubly Robust Augmented Model Accuracy Transfer Inference with High Dimensional Features

IF 3 1区数学 Q1 STATISTICS & PROBABILITY

Journal of the American Statistical Association Pub Date : 2024-05-21 DOI:10.1080/01621459.2024.2356291

Doudou Zhou, Molei Liu, Mengyan Li, Tianxi Cai

引用次数: 0

Abstract

Transfer learning is crucial for training models that generalize to unlabeled target populations using labeled source data, especially in real-world studies where label scarcity and covariate shift...

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具有高维特征的双稳健增强模型精度转移推理

迁移学习对于利用带标记的源数据训练可泛化到无标记目标人群的模型至关重要，尤其是在现实世界的研究中，因为在现实世界中，标记稀缺且共变因素会发生变化...

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of the American Statistical Association 数学-统计学与概率论

CiteScore

7.50

自引率

8.10%

发文量

168

审稿时长

12 months

期刊介绍： Established in 1888 and published quarterly in March, June, September, and December, the Journal of the American Statistical Association ( JASA ) has long been considered the premier journal of statistical science. Articles focus on statistical applications, theory, and methods in economic, social, physical, engineering, and health sciences. Important books contributing to statistical advancement are reviewed in JASA . JASA is indexed in Current Index to Statistics and MathSci Online and reviewed in Mathematical Reviews. JASA is abstracted by Access Company and is indexed and abstracted in the SRM Database of Social Research Methodology.