TY - JOUR
T1 - Bayesian generalizations of the integer-valued autoregressive model
AU - Marques F, Paulo C.
AU - Graziadei, Helton
AU - Lopes, Hedibert F.
N1 - Funding Information:
Helton Graziadei and Hedibert F. Lopes thank Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) for financial support through grants numbers 2017/10096-6 and 2017/22914-5.
Publisher Copyright:
© 2020 Informa UK Limited, trading as Taylor & Francis Group.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85090012626&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85090012626&partnerID=8YFLogxK
U2 - 10.1080/02664763.2020.1812544
DO - 10.1080/02664763.2020.1812544
M3 - Article
AN - SCOPUS:85090012626
SN - 0266-4763
VL - 49
SP - 336
EP - 356
JO - Journal of Applied Statistics
JF - Journal of Applied Statistics
IS - 2
ER -