Nonparametric and parametric estimation for a linear germination-growth model

S. N. Chiu*, M. P. Quine, M. Stewart

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

4 Citations (Scopus)
12 Downloads (Pure)

Abstract

Seeds are planted on the interval [0, L] at various locations. Each seed has a location x and a potential germination time t ε [0, ∞), and it is assumed that the collection of such (x, t) pairs forms a Poisson process in [0, L] x [0, ∞) with intensity measure dxdΛ(t). From each seed that germinates, an inhibiting region grows bidirectionally at rate 2v. These regions inhibit germination of any seed in the region with a later potential germination time. Thus, seeds only germinate in the uninhibited part of [0, L]. We want to estimate A on the basis of one or more realizations of the process, the data being the locations and germination times of the germinated seeds. We derive the maximum likelihood estimator of v and a nonparametric estimator of A and describe methods of obtaining parametric estimates from it, illustrating these with reference to gamma densities. Simulation results are described and the methods applied to some neurobiological data. An Appendix outlines the S-PLUS code used.

Original languageEnglish
Pages (from-to)755-760
Number of pages6
JournalBiometrics
Volume56
Issue number3
DOIs
Publication statusPublished - Sept 2000

Scopus Subject Areas

  • Statistics and Probability
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics

User-Defined Keywords

  • Boolean model
  • DNA replication
  • Germination-growth process
  • Inhibition
  • Maximum likelihood estimation
  • Nucleation
  • Synaptic transmission

Fingerprint

Dive into the research topics of 'Nonparametric and parametric estimation for a linear germination-growth model'. Together they form a unique fingerprint.

Cite this