| Full text | |
| Author(s): |
C. Marques F., Paulo
;
Graziadei, Helton
;
Lopes, Hedibert F.
Total Authors: 3
|
| Document type: | Journal article |
| Source: | Journal of Applied Statistics; v. 49, n. 2, p. 21-pg., 2020-08-29. |
| Abstract | |
We develop two Bayesian generalizations of the Poisson integer-valued autoregressive model. The AdINAR(1) model accounts for overdispersed data by means of an innovation process whose marginal distributions are finite mixtures, while the DP-INAR(1) model is a hierarchical extension involving a Dirichlet process, which is capable of modeling a latent pattern of heterogeneity in the distribution of the innovations rates. The probabilistic forecasting capabilities of both models are put to test in the analysis of crime data in Pittsburgh, with favorable results. (AU) | |
| FAPESP's process: | 17/10096-6 - Bayesian semiparametric analysis of autoregressive models |
| Grantee: | Helton Graziadei de Carvalho |
| Support Opportunities: | Scholarships in Brazil - Doctorate |
| FAPESP's process: | 17/22914-5 - Time-clustering and forecasting performance in semi-parametric INAR(1) models |
| Grantee: | Helton Graziadei de Carvalho |
| Support Opportunities: | Scholarships abroad - Research Internship - Doctorate |