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

We demonstrate the usefulness of submodularity in statistics as acharacterization of the difficulty of the \emph{search} problem of featureselection. The search problem is the ability of a procedure to identify aninformative set of features as opposed to the performance of the optimal set offeatures. Submodularity arises naturally in this setting due to its connectionto combinatorial optimization. In statistics, submodularity isolates cases inwhich collinearity makes the choice of model features difficult from those inwhich this task is routine. Researchers often report the signal-to-noise ratioto measure the difficulty of simulated data examples. A measure ofsubmodularity should also be provided as it characterizes an independentcomponent difficulty. Furthermore, it is closely related to other statisticalassumptions used in the development of the Lasso, Dantzig selector, and sureinformation screening.

Submodularity in Statistics: Comparing the Success of Model Selection Methods