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

Sparse Group LASSO (SGL) is a regularized model for high-dimensional linearregression problems with grouped covariates. SGL applies $l_1$ and $l_2$penalties on the individual predictors and group predictors, respectively, toguarantee sparse effects both on the inter-group and within-group levels. Inthis paper, we apply the approximate message passing (AMP) algorithm toefficiently solve the SGL problem under Gaussian random designs. We further usethe recently developed state evolution analysis of AMP to derive anasymptotically exact characterization of SGL solution. This allows us toconduct multiple fine-grained statistical analyses of SGL, through which weinvestigate the effects of the group information and $\gamma$ (proportion of$\ell_1$ penalty). With the lens of various performance measures, we show thatSGL with small $\gamma$ benefits significantly from the group information andcan outperform other SGL (including LASSO) or regularized models which do notexploit the group information, in terms of the recovery rate of signal, falsediscovery rate and mean squared error.

Asymptotic Statistical Analysis of Sparse Group LASSO via Approximate Message Passing Algorithm