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Model identification using the Efficient Determination Criterion

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Paulo Angelo Alves Resende, Chang Chung Yu Dorea

Statistics Theory · Submitted 25 Sep 2014

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim This framework is applied to BEKK multivariate GARCH models and, in particular, the strong consistency of the order estimator based on BIC is established for these models.

how it is stated · asserted 1.00 read the record →

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arXiv ID1409.7441 (version 2)
Pith IDV4CYULHB, Pith's permanent record of this paper and its review
CitationChang Chung Yu Dorea, Paulo Angelo Alves Resende. "Model identification using the Efficient Determination Criterion." arXiv:1409.7441 (2014). https://pith.science/paper/V4CYULHB
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Compiled by classifier-1.13-20260917 via OpenRouter, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,261 input tokens, $0.0002 reported.

What the abstract itself states, as probabilities: claims first 0.07 · states limitation 0.29 · human participants 0.02 · controlled comparison 0.02 · randomized assignment 0.01 · states quantitative result 0.05 · compares with prior results 0.37 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v2. Results and the main claim are underlined; .

In the realm of the model selection context, Akaike's and Schwarz's information criteria, AIC and BIC, have been applied successfully for decades for model order identification. The Efficient Determination Criterion (EDC) is a generalization of these criteria, proposed originally to define a strongly consistent class of estimators for the dependency order of a multiple Markov chain. In this work, the EDC is generalized to partially nested models, which encompass many other order identification problems. Based on some assumptions, a class of strongly consistent estimators is established in this general environment. This framework is applied to BEKK multivariate GARCH models and, in particular, the strong consistency of the order estimator based on BIC is established for these models.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 In the realm of the model selection context, Akaike's and Schwarz's information criteria, AIC and BIC, have been applied successfully for decades for model order identification. background 1.00 0.04 0.02 asserted 1.00
s1 The Efficient Determination Criterion (EDC) is a generalization of these criteria, proposed originally to define a strongly consistent class of estimators for the dependency order of a multiple Markov chain. background 0.97 0.06 0.04 asserted 1.00
s2 In this work, the EDC is generalized to partially nested models, which encompass many other order identification problems. contribution 0.99 0.90 0.13 asserted 1.00
s3 Based on some assumptions, a class of strongly consistent estimators is established in this general environment. result 0.77 0.87 0.30 asserted 1.00
s4 This framework is applied to BEKK multivariate GARCH models and, in particular, the strong consistency of the order estimator based on BIC is established for these models. result 0.76 0.89 0.47 asserted 1.00

What kind of paper the classifier reports this is: theoretical result 0.96 · evidence from proof or derivation 1.00.

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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

In the realm of the model selection context, Akaike's and Schwarz's information criteria, AIC and BIC, have been applied successfully for decades for model order identification. The Efficient Determination Criterion (EDC) is a generalization of these criteria, proposed originally to define a strongly consistent class of estimators for the dependency order of a multiple Markov chain. In this work, the EDC is generalized to partially nested models, which encompass many other order identification problems. Based on some assumptions, a class of strongly consistent estimators is established in this general environment. This framework is applied to BEKK multivariate GARCH models and, in particular, the strong consistency of the order estimator based on BIC is established for these models.

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