Department of Mathematics
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.
coverage, Johnson-Mehl model, Kernel smoothing, nonstationary Boolean model
Source Publication Title
Oxford University Press
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.
This research is supported by the UK/Hong Kong Joint Research Scheme.
Link to Publisher's Edition
Molchanov, I., & Chiu, S. (2000). Smoothing techniques and estimation methods for nonstationary Boolean models with applications to coverage processes. Biometrika, 87 (2), 265-283. https://doi.org/10.1093/biomet/87.2.265