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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

The solution of the penalized least squares problems depends on a tuning parameter. A popular tool for specifying the tuning parameter is the generalized cross-validation (GCV). In this work, we utilize estimates for the GCV function whose minimizers can lead to the determination of the tuning parameter. The selection of an efficient estimate depends on an appropriately defined index of proximity. Bounds and specific values are derived for this index, and a thorough study proves that the proposed one-term estimate suits perfectly to statistical models with high correlated variables. This is confirmed through simulation tests for several datasets.

Numerical methods for estimating the tuning parameter in penalized least squares problems