Aster models for life history analysis

Charles J. Geyer, Stuart Wagenius, Ruth G. Shaw

Research output: Contribution to journalArticlepeer-review

81 Scopus citations

Abstract

We present a new class of statistical models, designed for life history analysis of plants and animals, that allow joint analysis of data on survival and reproduction over multiple years, allow for variables having different probability distributions, and correctly account for the dependence of variables on earlier variables. We illustrate their utility with an analysis of data taken from an experimental study of Echinacea angustifolia sampled from remnant prairie populations in western Minnesota. These models generalize both generalized linear models and survival analysis. The joint distribution is factorized as a product of conditional distributions, each an exponential family with the conditioning variable being the sample size of the conditional distribution. The model may be heterogeneous, each conditional distribution being from a different exponential family. We show that the joint distribution is from a flat exponential family and derive its canonical parameters, Fisher information and other properties. These models are implemented in an R package 'aster' available from the Comprehensive R Archive Network, CRAN.

Original languageEnglish (US)
Pages (from-to)415-426
Number of pages12
JournalBiometrika
Volume94
Issue number2
DOIs
StatePublished - Jun 2007

Keywords

  • Conditional exponential family
  • Flat exponential family
  • Generalized linear model
  • Graphical model
  • Maximum likelihood

Fingerprint Dive into the research topics of 'Aster models for life history analysis'. Together they form a unique fingerprint.

Cite this