Prior sensitivity analysis in a semi-parametric integer-valued time series model

Helton Graziadei, Antonio Lijoi, Hedibert F. Lopes, Paulo C.F. Marques, Igor Prünster

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

We examine issues of prior sensitivity in a semi-parametric hierarchical extension of the INAR(p) model with innovation rates clustered according to a Pitman-Yor process placed at the top of the model hierarchy. Our main finding is a graphical criterion that guides the specification of the hyperparameters of the Pitman-Yor process base measure. We show how the discount and concentration parameters interact with the chosen base measure to yield a gain in terms of the robustness of the inferential results. The forecasting performance of the model is exemplified in the analysis of a time series of worldwide earthquake events, for which the new model outperforms the original INAR(p) model.

Original languageEnglish (US)
Pages (from-to)69
Number of pages1
JournalEntropy
Volume22
Issue number1
DOIs
StatePublished - Jan 1 2020
Externally publishedYes

Keywords

  • Bayesian forecasting
  • Bayesian hierarchical modeling
  • Bayesian nonparametrics
  • Clustering
  • Pitman-Yor process
  • Prior sensitivity
  • Time series of counts

ASJC Scopus subject areas

  • Information Systems
  • Mathematical Physics
  • Physics and Astronomy (miscellaneous)
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'Prior sensitivity analysis in a semi-parametric integer-valued time series model'. Together they form a unique fingerprint.

Cite this