Document Type

Journal Article

Department/Unit

Department of Mathematics

Language

English

Abstract

Kernel smoothing methods are applied to nonparametric estimation for nonstationary Boolean models. In many applications only exposed tangent points of the models are observable rather than full realisations. Several methods are developed for estimating the distribution of the underlying Boolean model from observation of the exposed tangent points. In particular, estimation methods for coverage processes are studied in detail and applied to neurobiological data.

Keywords

coverage, Johnson-Mehl model, Kernel smoothing, nonstationary Boolean model

Publication Date

6-2000

Source Publication Title

Biometrika

Volume

87

Issue

2

Start Page

265

End Page

283

Publisher

Oxford University Press

Peer Reviewed

1

Copyright

This is a pre-copyedited, author-produced version of an article accepted for publication in Biometrika following peer review. The version of record IS Molchanov, SN Chiu; Smoothing techniques and estimation methods for nonstationary Boolean models with applications to coverage processes. Biometrika 2000; 87 (2): 265-283. doi: 10.1093/biomet/87.2.265 is available online at: https://doi.org/10.1093/biomet/87.2.265.

Funder

This research is supported by the UK/Hong Kong Joint Research Scheme.

DOI

10.1093/biomet/87.2.265

Link to Publisher's Edition

https://doi.org/10.1093/biomet/87.2.265

ISSN (print)

00063444

ISSN (electronic)

14643510

Included in

Mathematics Commons

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