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/

Zendy Plus

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

Zendy Tools

Zendy Open

This paper studies linear reconstruction of partially observed functionaldata which are recorded on a discrete grid. We propose a novel estimationapproach based on approximate factor models with increasing rank taking intoaccount potential covariate information. Whereas alternative reconstructionprocedures commonly involve some preliminary smoothing, our method separatesthe signal from noise and reconstructs missing fragments at once. We establishuniform convergence rates of our estimator and introduce a new method forconstructing simultaneous prediction bands for the missing trajectories. Asimulation study examines the performance of the proposed methods in finitesamples. Finally, a real data application of temperature curves demonstratesthat our theory provides a simple and effective method to recover missingfragments.

Covariate-informed reconstruction of partially observed functional data via factor models