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

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)

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 languageEnglish
Pages (from-to)265-283
Number of pages19
JournalBiometrika
Volume87
Issue number2
DOIs
Publication statusPublished - 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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