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Minimax and Communication-Efficient Distributed Best Subset Selection with Oracle Property

Autor: Lan, Jingguo, Lin, Hongmei, Wang, Xueqin

The explosion of large-scale data in fields such as finance, e-commerce, and social media has outstripped the processing capabilities of single-machine systems, driving the need for distributed statistical inference methods. Traditional approaches to

Externí odkaz: http://arxiv.org/abs/2408.17276

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Sparsity-Constraint Optimization via Splicing Iteration

Autor: Wang, Zezhi, Zhu, Jin, Zhu, Junxian, Tang, Borui, Lin, Hongmei, Wang, Xueqin

Sparsity-constraint optimization has wide applicability in signal processing, statistics, and machine learning. Existing fast algorithms must burdensomely tune parameters, such as the step size or the implementation of precise stop criteria, which ma

Externí odkaz: http://arxiv.org/abs/2406.12017

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Best-Subset Selection in Generalized Linear Models: A Fast and Consistent Algorithm via Splicing Technique

Autor: Zhu, Junxian, Zhu, Jin, Tang, Borui, Chen, Xuanyu, Lin, Hongmei, Wang, Xueqin

In high-dimensional generalized linear models, it is crucial to identify a sparse model that adequately accounts for response variation. Although the best subset section has been widely regarded as the Holy Grail of problems of this type, achieving e

Externí odkaz: http://arxiv.org/abs/2308.00251

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