The DOI matches its Crossref record; 47 of the 48 references it cites were found in a registry or an index.
A new look at the statistical model identification
Abstract
The history of the development of statistical hypothesis testing in time series analysis is reviewed briefly and it is pointed out that the hypothesis testing procedure is not adequately defined as the procedure for statistical model identification. The classical maximum likelihood estimation procedure is reviewed and a new estimate minimum information theoretical criterion (AIC) estimate (MAICE) which is designed for the purpose of statistical identification is introduced. When there are several competing models the MAICE is defined by the model and the maximum likelihood estimates of the parameters which give the minimum of AIC defined by AIC = (-2)log-(maximum likelihood) + 2(number of independently adjusted parameters within the model). MAICE provides a versatile procedure for statistical model identification which is free from the ambiguities inherent in the application of conventional hypothesis testing procedure. The practical utility of MAICE in time series analysis is demonstrated with some numerical examples.
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In the citing paper
“Within a set of models for the same data, the Akaike Information Criterion (AIC; Akaike, 1974) and Expected Cross Validation Index (ECVI; Browne & Cudeck, 1993) can be used to compare competing models that need not be nested (smaller is better).”
From Nostalgia-Evoked Inspiration: Mediating Mechanisms and Motivational Implications (2015) · cites this paper as Akaike, 1974
In the citing paper
“In addition to the theoretical grounding, we further evaluated the appropriateness of using the OSMs with linear models (and linear mixed models) by computing the Akaike information criterion (AIC) [120] and the Bayesian information criterion (BIC) [121] of different models for attractiveness and each of the four dependent variables.”
From What is beautiful is still good: the attractiveness halo effect in the era of beauty filters (2024) · cites this paper as [120]
In the citing paper
“AIC measures each model's goodness of fit in proportion to the number of independent variables; when comparing models, the model with the minimal AIC is preferred (Akaike, 1974).”
From Measuring the Evolution of a Scientific Field through Citation Frames (2018) · cites this paper as Akaike, 1974
In the citing paper
“The quality of the statistical model was checked using the entropy maximization principle of the Akaike information criterion (AIC) (Akaike, 1974).”
From Persistent psychopathology and neurocognitive impairment in COVID-19 survivors: effect of inflammatory biomarkers at three-month follow-up (2021) · cites this paper as Akaike, 1974
In the citing paper
“Model fits were compared using the BIC and the AIC [34], which penalise models for the number of free parameters that are estimated to ensure that models with more parameters are not overfitted.”
From Neurocomputational mechanisms underlying subjective valuation of effort costs (2017) · cites this paper as [34]
In the citing paper
“The best model was identified using Akaike's (AIC) and Schwartz's information criteria (SBC or BIC) (45,46).”
From Influence of the COMT genotype on working memory and brain activity changes during development (2011) · cites this paper as [45]
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“In choosing between sub-models, variance components are excluded from the selection process if there is no significant deterioration in model fit after the component is dropped, as assessed by the Akaike Information Criterion (Akaike, 1974).”
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Cross-Validation Of Covariance Structures
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Metadata
- Authors
- Akaike, H.
- Affiliations
- The Institute of Statistical Mathematics (JP)
- Corresponding author
- Akaike, H.
- Venue
- IEEE Transactions on Automatic Control
- Published
- 1974-12
- Volume
- 19
- Issue
- 6
- Pages
- 716-723
- ISSN
- 0018-9286 (ISSN-L)
- OpenAlex
- W2142635246
- Cited by
- 51,879
- References
- 48
- Citations by year
- 2026: 2363 · 2025: 2646 · 2024: 2436 · 2023: 2545 · 2022: 2756 · 2021: 3085 · 2020: 2796 · 2019: 2643 · 2018: 2462 · 2017: 2474 · 2016: 2272 · 2015: 2369 · 2014: 2357 · 2013: 2274 · 2012: 2097
- FWCI
- 42.49
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Automatic Control (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Physical Sciences › Engineering › Control and Systems Engineering › Control Systems and Identification
- Physical Sciences › Mathematics › Applied Mathematics › Statistical and numerical algorithms
- Keywords
- time series analysis, hypothesis testing, maximum likelihood estimation, Akaike information criterion, model selection
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