Estimating a bivariate density when there are extra data on one or both components

Peter Hall and Natalie Neumeyer

Biometrika, 2006, vol. 93, issue 2, 439-450

Abstract: The objective of this paper is to estimate a bivariate density nonparametrically from a dataset from the joint distribution and datasets from one or both marginal distributions. We develop a copula-based solution, which has potential benefits even when the marginal datasets are empty. For example, if the copula density is sufficiently smooth in the region where we wish to estimate it, the joint density can be estimated with a high degree of accuracy. Similar improvements in performance are available if the marginals are close to being independent. We use wavelet estimators to approximate the copula density, which in cases of statistical interest can be unbounded along boundaries. Our techniques are also useful for solving recently-considered related problems, for example where the marginal distributions are determined by parametric models. The methodology is also readily extended to more general multivariate settings. Copyright 2006, Oxford University Press.

Date: 2006
References: Add references at CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://hdl.handle.net/10.1093/biomet/93.2.439 (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:oup:biomet:v:93:y:2006:i:2:p:439-450

Ordering information: This journal article can be ordered from
https://academic.oup.com/journals

Access Statistics for this article

Biometrika is currently edited by Paul Fearnhead

More articles in Biometrika from Biometrika Trust Oxford University Press, Great Clarendon Street, Oxford OX2 6DP, UK.
Bibliographic data for series maintained by Oxford University Press ().