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 adaptive maximum-likelihood-type estimators and adaptiveBayes-type ones for discretely observed ergodic diffusion processes withobservation noise whose variance is constant. The quasi-likelihood functionsfor the diffusion and drift parameters are introduced and the polynomial-typelarge deviation inequalities for those quasi-likelihoods are shown to see theconvergence of moments for those estimators.

Quasi-likelihood analysis of an ergodic diffusion plus noise