Cited by 41,495Statistical Methods and InferenceStatistical Methods and Bayesian InferenceStatistical Methods and Applications

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Model selection and multimodel inference: a practical information-theoretic approach.

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Guthery, Fred S., Burnham, Kenneth P., Anderson, David R.·2003·The Journal of Wildlife Management

DOI 10.2307/3802723

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Abstract

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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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)
DOI: 10.2307/3802723
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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Model selection and multimodel inference: a practical information-theoretic approach.

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