Smoothing techniques and estimation methods for nonstationary Boolean models with applications to coverage processes

I. S. Molchanov; Sung Nok CHIU

doi:10.1093/biomet/87.2.265

Smoothing techniques and estimation methods for nonstationary Boolean models with applications to coverage processes

I. S. Molchanov^*, Sung Nok CHIU

^*Corresponding author for this work

Department of Mathematics

Research output: Contribution to journal › Article › peer-review

9 Citations (Scopus)

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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.

Original language	English
Pages (from-to)	265-283
Number of pages	19
Journal	Biometrika
Volume	87
Issue number	2
DOIs	https://doi.org/10.1093/biomet/87.2.265
Publication status	Published - 2000

Scopus Subject Areas

Statistics and Probability
Mathematics(all)
Agricultural and Biological Sciences (miscellaneous)
Agricultural and Biological Sciences(all)
Statistics, Probability and Uncertainty
Applied Mathematics

User-Defined Keywords

Coverage
Johnson-Mehl model
Kernel smoothing
Nonstationary Boolean model

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10.1093/biomet/87.2.265

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AB - 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.

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Smoothing techniques and estimation methods for nonstationary Boolean models with applications to coverage processes

Abstract

Scopus Subject Areas

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