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 | |
| Publication status | Published - Jun 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