On the Empirical Bayesian Approach for the Poisson-Gaussian Model

Leonidas Sakalauskas ()
Additional contact information
Leonidas Sakalauskas: Institute of Mathematics and Informatics

Methodology and Computing in Applied Probability, 2010, vol. 12, issue 2, 247-259

Abstract: Abstract The paper considers numerical issues of the empirical Bayesian approach model, applied to estimation of small rates. The condition for non-singularity of Bayesian estimates is given and the convenient iterative algorithm for estimation is described. The clustering algorithm is also developed, using the property of Poisson-Gaussian model to treat probabilities of events in populations being the same, if the variance of probabilities is small. The approach considered is illustrated by an application to the analysis of homicides and suicides data in Lithuania, 2003–2004.

Keywords: Empirical Bayesian; Maximal likelihood; Clustering; Poisson- Gaussian model.; 62C12 (search for similar items in EconPapers)
Date: 2010
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://link.springer.com/10.1007/s11009-009-9146-2 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:metcap:v:12:y:2010:i:2:d:10.1007_s11009-009-9146-2

Ordering information: This journal article can be ordered from
https://www.springer.com/journal/11009

DOI: 10.1007/s11009-009-9146-2

Access Statistics for this article

Methodology and Computing in Applied Probability is currently edited by Joseph Glaz

More articles in Methodology and Computing in Applied Probability from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().