Non-parametric identification and estimation of the number of components in multivariate mixtures

Hiroyuki Kasahara and Katsumi Shimotsu

Journal of the Royal Statistical Society Series B, 2014, vol. 76, issue 1, 97-111

Abstract: type="main" xml:id="rssb12022-abs-0001">

We analyse the identifiability of the number of components in k-variate, M-component finite mixture models in which each component distribution has independent marginals, including models in latent class analysis. Without making parametric assumptions on the component distributions, we investigate how one can identify the number of components from the distribution function of the observed data. When k≥2, a lower bound on the number of components (M) is non-parametrically identifiable from the rank of a matrix constructed from the distribution function of the observed variables. Building on this identification condition, we develop a procedure to estimate a lower bound on the number of components consistently.

Date: 2014
References: Add references at CitEc
Citations: View citations in EconPapers (24)

Downloads: (external link)
http://hdl.handle.net/10.1111/rssb.2013.76.issue-1 (text/html)
Access to full text is restricted to subscribers.

Related works:
Working Paper: Nonparametric Identification and Estimation of the Number of Components in Multivariate Mixtures (2012)
Working Paper: Nonparametric Identification and Estimation of the Number of Components in Multivariate Mixtures (2012)
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:bla:jorssb:v:76:y:2014:i:1:p:97-111

Ordering information: This journal article can be ordered from
http://ordering.onli ... 1111/(ISSN)1467-9868

Access Statistics for this article

Journal of the Royal Statistical Society Series B is currently edited by P. Fryzlewicz and I. Van Keilegom

More articles in Journal of the Royal Statistical Society Series B from Royal Statistical Society Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery ().