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

This work aims at making a comprehensive contribution in the general area ofparametric inference for discretely observed diffusion processes. Establishedapproaches for likelihood-based estimation invoke a time-discretisation schemefor the approximation of the intractable transition dynamics of the StochasticDifferential Equation (SDE) model over finite time periods. The scheme isapplied for a step-size that is either user-selected or determined by the data.Recent research has highlighted the critical ef-fect of the choice of numericalscheme on the behaviour of derived parameter estimates in the setting ofhypo-elliptic SDEs. In brief, in our work, first, we develop two weak secondorder sampling schemes (to cover both hypo-elliptic and elliptic SDEs) andproduce a small time expansion for the density of the schemes to form a proxyfor the true intractable SDE transition density. Then, we establish acollection of analytic results for likelihood-based parameter estimatesobtained via the formed proxies, thus providing a theoretical framework thatshowcases advantages from the use of the developed methodology for SDEcalibration. We present numerical results from carrying out classical orBayesian inference, for both elliptic and hypo-elliptic SDEs.

Parameter Estimation with Increased Precision for Elliptic and Hypo-elliptic Diffusions