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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

This article improves on existing methods to estimate the spectral density ofstationary and nonstationary time series assuming a Gaussian process prior. Byoptimising an appropriate eigendecomposition using a smoothing splinecovariance structure, our method more appropriately models data with bothsimple and complex periodic structure. We further justify the utility of thisoptimal eigendecomposition by investigating the performance of alternativecovariance functions other than smoothing splines. We show that the optimaleigendecomposition provides a material improvement, while the other covariancefunctions under examination do not, all performing comparatively well as thesmoothing spline. During our computational investigation, we introduce newvalidation metrics for the spectral density estimate, inspired from thephysical sciences. We validate our models in an extensive simulation study anddemonstrate superior performance with real data.

Optimally adaptive Bayesian spectral density estimation for stationary and nonstationary processes