Evidence map›Paper›PMID 33286221›Full record

ArticleEntropy (Basel, Switzerland)2020

Upgrading Model Selection Criteria with Goodness of Fit Tests for Practical Applications.

Riccardo Rossi, Andrea Murari, Pasquale Gaudio, Michela Gelfusa

Abstract read
In one paragraph

Article in Entropy (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Riccardo RossiDepartment of Industrial Engineering, University of Rome "Tor Vergata", via del Politecnico 1, 01100 Roma, Italy.ORCID 0000-0003-4414-6119
Andrea MurariConsorzio RFX (CNR, ENEA, INFN, Università di Padova, Acciaierie Venete SpA), Corso Stati Uniti 4, 35127 Padova, Italy.
Pasquale GaudioDepartment of Industrial Engineering, University of Rome "Tor Vergata", via del Politecnico 1, 01100 Roma, Italy.ORCID 0000-0003-0861-558X
Michela GelfusaDepartment of Industrial Engineering, University of Rome "Tor Vergata", via del Politecnico 1, 01100 Roma, Italy.ORCID 0000-0001-5158-7292

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The Bayesian information criterion (BIC), the Akaike information criterion (AIC), and some other indicators derived from them are widely used for model selection. In their original form, they contain the likelihood of the data given the models. Unfortunately, in many applications, it is practically impossible to calculate the likelihood, and, therefore, the criteria have been reformulated in terms of descriptive statistics of the residual distribution: the variance and the mean-squared error of the residuals. These alternative versions are strictly valid only in the presence of additive noise of Gaussian distribution, not a completely satisfactory assumption in many applications in science and engineering. Moreover, the variance and the mean-squared error are quite crude statistics of the residual distributions. More sophisticated statistical indicators, capable of better quantifying how close the residual distribution is to the noise, can be profitably used. In particular, specific goodness of fit tests have been included in the expressions of the traditional criteria and have proved to be very effective in improving their discriminating capability. These improved performances have been demonstrated with a systematic series of simulations using synthetic data for various classes of functions and different noise statistics.

Indexed as

Bayesian information criterion (BIC), Akaike information criterion (AIC), Shannon entropygoodness of fit testsKolmogorov–Smirnov testmodel selection criteria

Identifiers

PMID33286221
PMCPMC7516921

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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.