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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

In this paper we study grouped variable selection problems by proposing aspecified prior, called the nested spike and slab prior, to model collectivebehavior of regression coefficients. At the group level, the nested spike andslab prior puts positive mass on the event that the l2-norm of the groupedcoefficients is equal to zero. At the individual level, each coefficient isassumed to follow a spike and slab prior. We carry out maximum a posterioriestimation for the model by applying blockwise coordinate descent algorithms tosolve an optimization problem involving an approximate objective modified bymajorization-minimization techniques. Simulation studies show that the proposedestimator performs relatively well in the situations in which the true andredundant covariates are both covered by the same group. Asymptotic analysisunder a frequentist's framework further shows that the l2 estimation error ofthe proposed estimator can have a better upper bound if the group that coversthe true covariates does not cover too many redundant covariates. In addition,given some regular conditions hold, the proposed estimator is asymptoticallyinvariant to group structures, and its model selection consistency can beestablished without imposing irrepresentable-type conditions.

Grouped Variable Selection via Nested Spike and Slab Priors