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/

Zendy Plus

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

Zendy Tools

Zendy Open

The parametric estimators applied by rolling are commonly used in theanalysis of time series with nonlinear features, such as structural change dueto time varying parameters and local trends. This paper examines the propertiesof rolling estimators in the class of Temporally Local Maximum Likelihood(TLML) estimators. We study the TLML estimators of constant parameters,stochastic and stationary parameters and parameters with the Ultra Long Run(ULR) dynamics bridging the gap between the constant and stochastic parameters.Moreover, we explore the properties of TLML estimators in an application to theSusceptible-Infected-Susceptible (SIS) epidemiological model and illustratetheir finite sample performance in a simulation study.

Temporally Local Maximum Likelihood with Application to SIS Model