Nonparametric Estimation of Multivariate Mixtures

Chaowen Zheng and Yichao Wu

Journal of the American Statistical Association, 2020, vol. 115, issue 531, 1456-1471

Abstract: A multivariate mixture model is determined by three elements: the number of components, the mixing proportions, and the component distributions. Assuming that the number of components is given and that each mixture component has independent marginal distributions, we propose a nonparametric method to estimate the component distributions. The basic idea is to convert the estimation of component density functions to a problem of estimating the coordinates of the component density functions with respect to a good set of basis functions. Specifically, we construct a set of basis functions by using conditional density functions and try to recover the coordinates of component density functions with respect to this set of basis functions. Furthermore, we show that our estimator for the component density functions is consistent. Numerical studies are used to compare our algorithm with other existing nonparametric methods of estimating component distributions under the assumption of conditionally independent marginals.

Date: 2020
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/01621459.2019.1635481 (text/html)
Access to full text is restricted to subscribers.

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:taf:jnlasa:v:115:y:2020:i:531:p:1456-1471

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/UASA20

DOI: 10.1080/01621459.2019.1635481

Access Statistics for this article

Journal of the American Statistical Association is currently edited by Xuming He, Jun Liu, Joseph Ibrahim and Alyson Wilson

More articles in Journal of the American Statistical Association from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().