Name
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.3390/e22010069 | ENTROPY |
Keywords | Field | DocType |
time series of counts,Bayesian hierarchical modeling,Bayesian nonparametrics,Pitman-Yor process,prior sensitivity,clustering,Bayesian forecasting | Integer,Time series,Mathematical optimization,Hyperparameter,Robustness (computer science),Semiparametric model,Bayesian hierarchical modeling,Cluster analysis,Pitman–Yor process,Mathematics | Journal |
Volume | Issue | ISSN |
22 | 1 | 1099-4300 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Helton Graziadei | 1 | 0 | 0.34 |
| Antonio Lijoi | 2 | 30 | 5.70 |
| Hedibert Freitas Lopes | 3 | 0 | 0.34 |
| Paulo C. Marques F. | 4 | 0 | 0.34 |
| Prünster Igor | 5 | 17 | 2.59 |