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

Robust time series analysis is an important subject in statistical modeling.Models based on Gaussian distribution are sensitive to outliers, which mayimply in a significant degradation in estimation performance as well as inprediction accuracy. State-space models, also referred as Dynamic Models, is avery useful way to describe the evolution of a time series variable through astructured latent evolution system. Integrated Nested Laplace Approximation(INLA) is a recent approach proposed to perform fast Bayesian inference inLatent Gaussian Models which naturally comprises Dynamic Models. We present howto perform fast and accurate non-Gaussian dynamic modeling with INLA and showhow these models can provide a more robust time series analysis when comparedwith standard dynamic models based on Gaussian distributions. We formalize theframework used to fit complex non-Gaussian space-state models using the Rpackage INLA and illustrate our approach in both a simulation study and on thebrazilian homicide rate dataset.

Inference on Dynamic Models for non-Gaussian Random Fields using INLA: A Homicide Rate Analysis of Brazilian Cities