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

We study a nonparametric regression model for sample data which is defined onan $N$-dimensional lattice structure and which is assumed to be strong spatialmixing: we use design adapted multidimensional Haar wavelets which form anorthonormal system w.r.t. the empirical measure of the sample data. For suchorthonormal systems, we consider a nonparametric hard thresholding estimator.We give sufficient criteria for the consistency of this estimator and we deriverates of convergence. The theorems reveal that our estimator is able to adaptto the local smoothness of the underlying regression function and the designdistribution. We illustrate our results with simulated examples.

Orthogonal Series Estimates on Strong Spatial Mixing Data