English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Distributed statistical learning has become a popular technique forlarge-scale data analysis. Most existing work in this area focuses on dividingthe observations, but we propose a new algorithm, DDAC-SpAM, which divides thefeatures under a high-dimensional sparse additive model. Our approach involvesthree steps: divide, decorrelate, and conquer. The decorrelation operationenables each local estimator to recover the sparsity pattern for each additivecomponent without imposing strict constraints on the correlation structureamong variables. The effectiveness and efficiency of the proposed algorithm aredemonstrated through theoretical analysis and empirical results on bothsynthetic and real data. The theoretical results include both the consistentsparsity pattern recovery as well as statistical inference for each additivefunctional component. Our approach provides a practical solution for fittingsparse additive models, with promising applications in a wide range of domains.

DDAC-SpAM: A Distributed Algorithm for Fitting High-dimensional Sparse Additive Models with Feature Division and Decorrelation