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

This article deals with the problem of functional classification forL2-valued random covariates when some of the covariates may have missing orunobservable fragments. Here, it is allowed for both the training sample aswell as the new unclassified observation to have missing fragments in theirfunctional covariates. Furthermore, unlike most previous results in theliterature, where covariate fragments are typically assumed to be missingcompletely at random, we do not impose any such assumptions here. Given theobserved segments of the curves, we construct a kernel-type classifier which isquite straightforward to implement in practice. We also establish the strongconsistency of the proposed classifier and provide some numerical examples toassess its performance in practice.

Statistical classification for partially observed functional data via filtering