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

In Selk and Gertheiss (2022) a nonparametric prediction method for modelswith multiple functional and categorical covariates is introduced. Thedependent variable can be categorical (binary or multi-class) or continuous,thus both classification and regression problems are considered. In the paperat hand the asymptotic properties of this method are developed. A uniform rateof convergence for the regression / classification estimator is given. Furtherit is shown that, asymptotically, a data-driven least squares cross-validationmethod can automatically remove irrelevant, noise variables.

Uniform convergence rates and automatic variable selection in nonparametric regression with functional and categorical covariates