Location of Repository

Using small bias nonparametric density estimators for confidence interval estimation

By M Di Marzio and CC Taylor

Abstract

Confidence intervals for densities built on the basis of standard nonparametric theory are doomed to have poor coverage rates due to bias. Studies on coverage improvement exist, but reasonably behaved interval estimators are needed. We explore the use of small bias kernel-based methods to construct confidence intervals, in particular using a geometric density estimator that seems better suited for this purpose

Publisher: Taylor & Francis Ltd
Year: 2009
OAI identifier: oai:eprints.whiterose.ac.uk:42950

Suggested articles

Preview


To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.