Dependence on a collection of Poisson random variables

Luis E. Nieto-Barajas ()
Additional contact information
Luis E. Nieto-Barajas: ITAM

Statistical Methods & Applications, 2022, vol. 31, issue 1, No 2, 39 pages

Abstract: Abstract We propose two novel ways of introducing dependence among Poisson counts through the use of latent variables in a three levels hierarchical model. Marginal distributions of the random variables of interest are Poisson with strict stationarity as special case. Order–p dependence is described in detail for a temporal sequence of random variables. A full Bayesian inference of the models is described and performance of the models is illustrated with a numerical analysis of maternal mortality in Mexico. Extensions to seasonal, periodic, spatial or spatio-temporal dependencies, as well as coping with overdispersion, are also discussed.

Keywords: Autoregressive process; Integer-valued time series; Latent variables; Moving average process; Stationary process (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10260-021-00561-x Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:stmapp:v:31:y:2022:i:1:d:10.1007_s10260-021-00561-x

Ordering information: This journal article can be ordered from
http://www.springer. ... cs/journal/10260/PS2

DOI: 10.1007/s10260-021-00561-x

Access Statistics for this article

Statistical Methods & Applications is currently edited by Tommaso Proietti

More articles in Statistical Methods & Applications from Springer, Società Italiana di Statistica
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().