Estimating linear functionals in Poisson mixture models
Laurent Cavalier and
Nicolas Hengartner
Journal of Nonparametric Statistics, 2009, vol. 21, issue 6, 713-728
Abstract:
This paper concerns the problem of estimating linear functionals of the mixing distribution from Poisson mixture observations. In particular, linear functionals for which a parametric rate of convergence cannot be achieved are studied. It appears that Gaussian functionals are rather easy to estimate. Estimation of the distribution functions is then considered by approximating this functional using Gaussian functionals. Finally, the case of smooth distribution functions is considered in order to deal with rather general linear functionals.
Date: 2009
References: View references in EconPapers View complete reference list from CitEc
Citations:
Downloads: (external link)
http://hdl.handle.net/10.1080/10485250902971716 (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:gnstxx:v:21:y:2009:i:6:p:713-728
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/GNST20
DOI: 10.1080/10485250902971716
Access Statistics for this article
Journal of Nonparametric Statistics is currently edited by Jun Shao
More articles in Journal of Nonparametric Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().