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MODEL SELECTION IN PHYLOGENETICS

Investigation into model selection has a long history in the statistical literature. As model-based approaches begin dominating systematic biology, increased attention has focused on how models should be selected for distance-based, likelihood, and Bayesian phylogenetics. Here, we review issues that... Full description

Journal Title: Annual review of ecology evolution, and systematics, 2005-12-15, Vol.36 (1), p.445-466
Main Author: Sullivan, Jack
Other Authors: Joyce, Paul
Format: Electronic Article Electronic Article
Language: English
Subjects:
AIC
BIC
DNA
Publisher: Palo Alto, CA: Annual Reviews
ID: ISSN: 1543-592X
Link: http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=17350747
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recordid: cdi_proquest_journals_219529996
title: MODEL SELECTION IN PHYLOGENETICS
format: Article
creator:
  • Sullivan, Jack
  • Joyce, Paul
subjects:
  • AIC
  • Analysis
  • Animal and plant ecology
  • Animal, plant and microbial ecology
  • BIC
  • Biological and medical sciences
  • Biology
  • Data analysis
  • Data models
  • Datasets
  • Decision theory
  • Deoxyribonucleic acid
  • DNA
  • Evolution
  • Fundamental and applied biological sciences. Psychology
  • General aspects
  • likelihood ratio
  • Modeling
  • Nature
  • Nucleotides
  • Parametric models
  • Phylogenetics
  • Phylogeny
  • Phylogeny (Botany)
  • Posadas
  • Quantitative genetics
  • Science
  • statistical phylogenetics
  • Studies
  • Taxonomy
  • Topology
ispartof: Annual review of ecology, evolution, and systematics, 2005-12-15, Vol.36 (1), p.445-466
description: Investigation into model selection has a long history in the statistical literature. As model-based approaches begin dominating systematic biology, increased attention has focused on how models should be selected for distance-based, likelihood, and Bayesian phylogenetics. Here, we review issues that render model-based approaches necessary, briefly review nucleotide-based models that attempt to capture relevant features of evolutionary processes, and review methods that have been applied to model selection in phylogenetics: likelihood-ratio tests, AIC, BIC, and performance-based approaches.
language: eng
source:
identifier: ISSN: 1543-592X
fulltext: no_fulltext
issn:
  • 1543-592X
  • 1545-2069
url: Link


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descriptionInvestigation into model selection has a long history in the statistical literature. As model-based approaches begin dominating systematic biology, increased attention has focused on how models should be selected for distance-based, likelihood, and Bayesian phylogenetics. Here, we review issues that render model-based approaches necessary, briefly review nucleotide-based models that attempt to capture relevant features of evolutionary processes, and review methods that have been applied to model selection in phylogenetics: likelihood-ratio tests, AIC, BIC, and performance-based approaches.
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subjectAIC ; Analysis ; Animal and plant ecology ; Animal, plant and microbial ecology ; BIC ; Biological and medical sciences ; Biology ; Data analysis ; Data models ; Datasets ; Decision theory ; Deoxyribonucleic acid ; DNA ; Evolution ; Fundamental and applied biological sciences. Psychology ; General aspects ; likelihood ratio ; Modeling ; Nature ; Nucleotides ; Parametric models ; Phylogenetics ; Phylogeny ; Phylogeny (Botany) ; Posadas ; Quantitative genetics ; Science ; statistical phylogenetics ; Studies ; Taxonomy ; Topology
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abstractInvestigation into model selection has a long history in the statistical literature. As model-based approaches begin dominating systematic biology, increased attention has focused on how models should be selected for distance-based, likelihood, and Bayesian phylogenetics. Here, we review issues that render model-based approaches necessary, briefly review nucleotide-based models that attempt to capture relevant features of evolutionary processes, and review methods that have been applied to model selection in phylogenetics: likelihood-ratio tests, AIC, BIC, and performance-based approaches.
copPalo Alto, CA
pubAnnual Reviews
doi10.1146/annurev.ecolsys.36.102003.152633