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

We consider the problem of estimating the predictive density of futureobservations from a non-parametric regression model. The density estimators areevaluated under Kullback--Leibler divergence and our focus is on establishingthe exact asymptotics of minimax risk in the case of Gaussian errors. We derivethe convergence rate and constant for minimax risk among Bayesian predictivedensities under Gaussian priors and we show that this minimax risk isasymptotically equivalent to that among all density estimators.

Asymptotic minimax risk of predictive density estimation for non-parametric regression