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

In this study, we develop a new theory of estimating Hurst parame- ter usingconic multivariate adaptive regression splines (CMARS) method. We concentrateon the strong solution of stochastic differentional equations (SDEs) driven byfractional Brownian motion (fBm). The superiority of our approach to the othersis, it not only estimates the Hurst parameter but also finds spline parametersof the stochastic process in an adaptive way. We examine the performance of ourestimations using simulated test data. Keywords: Stochastic differentialequations, fractional Brownian motion, Hurst parameter, conic multivariateadaptive regression splines

Estimation of Hurst Parameter of Fractional Brownian Motion Using CMARS Method