ArticleEntropy (Basel, Switzerland)2019
On the Use of Entropy to Improve Model Selection Criteria.
Article in Entropy (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
7 citing papers in PubMed.
- Statistics in Service of Metascience: Measuring Replication Distance with Reproducibility Rate.Entropy (Basel, Switzerland) · 2024Article
- Improved Treatment of the Independent Variables for the Deployment of Model Selection Criteria in the Analysis of Complex Systems.Entropy (Basel, Switzerland) · 2021Article
- Bridging Offline Functional Model Carrying Aging-Specific Growth Rate Information and Recombinant Protein Expression: Entropic Extension of Akaike Information Criterion.Entropy (Basel, Switzerland) · 2021Article
- A Task-Learning Strategy for Robotic Assembly Tasks from Human Demonstrations.Sensors (Basel, Switzerland) · 2020Article
- BayesianEntropy (Basel, Switzerland) · 2020Article
- Upgrading Model Selection Criteria with Goodness of Fit Tests for Practical Applications.Entropy (Basel, Switzerland) · 2020Article
- Model Selection in a Composite Likelihood Framework Based on Density Power Divergence.Entropy (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The most widely used forms of model selection criteria, the Bayesian Information Criterion (BIC) and the Akaike Information Criterion (AIC), are expressed in terms of synthetic indicators of the residual distribution: the variance and the mean-squared error of the residuals respectively. In many applications in science, the noise affecting the data can be expected to have a Gaussian distribution. Therefore, at the same level of variance and mean-squared error, models, whose residuals are more uniformly distributed, should be favoured. The degree of uniformity of the residuals can be quantified by the Shannon entropy. Including the Shannon entropy in the BIC and AIC expressions improves significantly these criteria. The better performances have been demonstrated empirically with a series of simulations for various classes of functions and for different levels and statistics of the noise. In presence of outliers, a better treatment of the errors, using the Geodesic Distance, has proved essential.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.