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

An objective Bayesian approach to estimate the number of degrees of freedom$(\nu)$ for the multivariate $t$ distribution and for the $t$-copula, when theparameter is considered discrete, is proposed. Inference on this parameter hasbeen problematic for the multivariate $t$ and, for the absence of any method,for the $t$-copula. An objective criterion based on loss functions which allowsto overcome the issue of defining objective probabilities directly is employed.The support of the prior for $\nu$ is truncated, which derives from theproperty of both the multivariate $t$ and the $t$-copula of convergence tonormality for a sufficiently large number of degrees of freedom. Theperformance of the priors is tested on simulated scenarios. The R codes and thereplication material are available as a supplementary material of theelectronic version of the paper and on real data: daily logarithmic returns ofIBM and of the Center for Research in Security Prices Database.

Objective priors for the number of degrees of freedom of a multivariate t distribution and the t-copula