Hypothesis tests on mixture model components with applications in ecology and agriculture

Ling Xu, Timothy Hanson, Edward J. Bedrick, Carla Restrepo

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Multiple comparisons are widely used to compare gross features of distributions across populations. However, often a scientific hypothesis is more easily couched in terms of more focused null and alternative statistical hypotheses. For example, among distributions exhibiting clusters of continuous measurements across strata, are there clusters of measurements similar in terms of location, spread, or weight? We propose testing such hypotheses using a sequence of nested finite mixture models. Reasonable, data-driven priors are suggested based on estimates of the sample spreads and midpoints. Formal hypothesis testing is carried out through the computation of Bayes factors. The method is illustrated on Holling's (EcologicalMonographs 62:447-502, 1992) forest and prairie bird body mass data, and data on the time-to-abortion in dairy cows. Supplemental simulations are available online.

Original languageEnglish (US)
Pages (from-to)308-326
Number of pages19
JournalJournal of Agricultural, Biological, and Environmental Statistics
Volume15
Issue number3
DOIs
StatePublished - 2010
Externally publishedYes

Keywords

  • Finite mixture model
  • Hierarchical mixture of experts
  • Multiple comparisons
  • Textural-discontinuity hypothesis

ASJC Scopus subject areas

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

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