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 establish some new non-asymptotical lower bounds for deviation of regularunbiased estimation of unknown parameter from its true value in differentnorms, alike the classical Rao-Kramer's inequality.  We show that if the new norm is weaker that ordinary Hilbertian norm, thatthe rate of convergence of arbitrary regular unbiased estimate does not exceed$ 1/\sqrt{n}, $ and if the new norm is stronger that one, the rate ofconvergence of the well-known Maximal Likelihood Estimate (MLE) is also equalto $ 1/\sqrt{n}.

Lower boundaries for parametric estimations in different norms