Efficient Fused Learning for Distributed Imbalanced Data
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Officially Accepted for Publication

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AbstractAny data set exhibiting an unequal or highly-skewed distribution between its classes/categories can be regarded as imbalanced data. Due to privacy concern and other technical limitations, imbalanced data distributed across locations/machines cannot be simply combined and stored in a single central location. The commonly used naive averaging estimate may be unstable for imbalanced data. In this paper, we propose a fused estimation for logistic regression in analyzing distributed imbalanced data by combining all the cases available on all machines, which is stable and efficient. The consistency and asymptotic normality of the proposed estimator are established under regularity conditions. Asymptotic efficiency compared with the oracle estimator based on the entire imbalanced data is also studied. Extensive simulation studies show that the proposed estimator is as efficient as the oracle estimator in various situations. An application is illustrated with a credit card data for default payment.
Acceptance Date29/01/2020
All Author(s) ListJie Zhou, Guohao Shen, Xuan Chen, Yuanyuan Lin
Journal nameCommunications in Statistics - Theory and Methods
Detailed descriptionThe article was accepted on Jan 29, 2020.
LanguagesEnglish-United States
KeywordsCase-control studies, Distributed imbalanced data, Logistic regression, Oracle estimator

Last updated on 2020-23-10 at 00:21