L2E estimation of mixture complexity for count data

T. Umashanger and T.N. Sriram

Computational Statistics & Data Analysis, 2009, vol. 53, issue 12, 4243-4254

Abstract: For count data, robust estimation of the number of mixture components in finite mixtures is revisited using L2 distance. An information criterion based on L2 distance is shown to yield an estimator, which is also shown to be strongly consistent. Monte Carlo simulations show that our estimator is competitive with other procedures in correctly determining the number of components when the data comes from Poisson mixtures. When the data comes from a negative binomial mixture but the postulated model is a Poisson mixture, simulations show that our estimator is highly competitive with the minimum Hellinger distance (MHD) estimator in terms of robustness against model misspecification. Furthermore, we illustrate the performance of our estimator for a real dataset with overdispersion and zero-inflation. Computational simplicity combined with robustness property makes the L2E approach an attractive alternative to other procedures in the literature.

Date: 2009
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167-9473(09)00202-3
Full text for ScienceDirect subscribers only.

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:eee:csdana:v:53:y:2009:i:12:p:4243-4254

Access Statistics for this article

Computational Statistics & Data Analysis is currently edited by S.P. Azen

More articles in Computational Statistics & Data Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().