A primer on model selection using the Akaike Information Criterion [PDF]
Infectious Disease Modelling, 2020 A powerful investigative tool in biology is to consider not a single mathematical model but a collection of models designed to explore different working hypotheses and select the best model in that collection.
Stéphanie Portet
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Cost-effectiveness of community diabetes screening: Application of Akaike information criterion in rural communities of Nigeria [PDF]
Frontiers in Public Health, 2022 BackgroundThe prevalence of diabetes mellitus (DM) is increasing globally, and this requires several approaches to screening. There are reports of alternative indices for prediction of DM, besides fasting blood glucose (FBG) level.
Anayochukwu Edward Anyasodor +4 more
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An Investigation of Extended-Dimension Embedded CKF-SLAM Based on the Akaike Information Criterion [PDF]
SensorsSimultaneous localization and mapping (SLAM) faces significant challenges due to high computational costs, low accuracy, and instability, which are particularly problematic because SLAM systems often operate in real-time environments where timely and ...
Hanghang Xu +3 more
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Bridging Offline Functional Model Carrying Aging-Specific Growth Rate Information and Recombinant Protein Expression: Entropic Extension of Akaike Information Criterion [PDF]
Entropy, 2021 This study presents a mathematical model of recombinant protein expression, including its development, selection, and fitting results based on seventy fed-batch cultivation experiments from two independent biopharmaceutical sites.
Renaldas Urniezius +2 more
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Performance of Akaike Information Criterion and Bayesian Information Criterion in Selecting Partition Models and Mixture Models. [PDF]
Syst Biol, 2023 AbstractIn molecular phylogenetics, partition models and mixture models provide different approaches to accommodating heterogeneity in genomic sequencing data. Both types of models generally give a superior fit to data than models that assume the process of sequence evolution is homogeneous across sites and lineages.
Liu Q +3 more
europepmc +3 more sources
Akaike's Information Criterion for Stoichiometry Inference of Supramolecular Complexes
Angewandte Chemie - International Edition, 2023 Abstract“How do we decide the stoichiometry of host–guest complexes?” This question has long been answered by the Job plot since its first report in 1928. However, as the Job plot was claimed to be misleading in 2016, the question became an open question again and called for renewed investigations.
Koki Ikemoto, Hiroyuki Isobe
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Application of the Akaike Information Criterion to Ultrasonic Measurement of Liquid Volume in a Cylindrical Tank [PDF]
SensorsThe ultrasonic sensor method is the most significant and widely accepted technique for measuring liquid levels in tanks. Ultrasonic waves are particularly advantageous in the case of explosive, flammable, or aggressive liquids because of the possibility ...
Krzysztof J. Opieliński +1 more
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Inadmissibility of the corrected Akaike information criterion
BernoulliFor the multivariate linear regression model with unknown covariance, the corrected Akaike information criterion is the minimum variance unbiased estimator of the expected Kullback--Leibler discrepancy. In this study, based on the loss estimation framework, we show its inadmissibility as an estimator of the Kullback--Leibler discrepancy itself, instead
Takeru Matsuda
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On the Akaike Information Criterion for choosing models for variograms of soil properties
Journal of Soil Science, 1989 SUMMARY A problem in the application of geostatistics to soil is to find satisfactory models for variograms of soil properties. It is usually solved by fitting plausible models to the sample variogram by weighted least squares approximation.
Webster, R., McBratney, A. B.
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Finite Sample Improvement of Akaike’s Information Criterion [PDF]
IEEE Transactions on Information Theory, 2021 We emphasize that it is possible to improve the principle of unbiased risk estimation for model selection by addressing excess risk deviations in the design of penalization procedures. Indeed, we propose a modification of Akaike's Information Criterion that avoids overfitting, even when the sample size is small.
Adrien Saumard, Fabien Navarro
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