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Model selection and multimodel inference: a practical information-theoretic approach.
Open accessAbstract
Introduction * Information and Likelihood Theory: A Basis for Model Selection and Inference * Basic Use of the Information-Theoretic Approach * Formal Inference From More Than One Model: Multi-Model Inference (MMI) * Monte Carlo Insights and Extended Examples * Statistical Theory and Numerical Results * Summary
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“Ranking these models using the sample-size-corrected Akaike Information Criterion (AICc) [29] revealed that the number of geckos on a tree was principally a function of the tree's size (estimated surface area of the main stem) and its proximity to the nearest termite mound (Table S1).”
From Spatial Pattern Enhances Ecosystem Functioning in an African Savanna (2010) · cites this paper as [29]
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Posada, D. (2008) · Molecular Biology and Evolution · doi:10.1093/molbev/msn083 · Cited by 10,578
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Novel methods improve prediction of species’ distributions from occurrence data
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Metadata
- Authors
- Guthery, F. S., Burnham, K. P., & Anderson, D. R.
- Venue
- The Journal of Wildlife Management
- Publisher
- JSTOR
- Published
- 2003-07
- Volume
- 67
- Issue
- 3
- Pages
- 655
- ISSN
- 0022-541X (ISSN-L)
- OpenAlex
- W2009435671
- Cited by
- 41,495
- References
- 0
- Citations by year
- 2026: 22 · 2025: 843 · 2024: 1223 · 2023: 1355 · 2022: 1673 · 2021: 2532 · 2020: 2605 · 2019: 2743 · 2018: 2552 · 2017: 2708 · 2016: 2821 · 2015: 2984 · 2014: 2929 · 2013: 2592 · 2012: 2272
- FWCI
- 648.08
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Journal of Wildlife Management (publishedVersion, cc-by)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Keywords
- model selection, multi-model inference, information theory, likelihood theory, Akaike information criterion, AICc, model averaging
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