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COMPARISON OF SOME INFORMATION CRITERIA WITH THE DEVELOPED RANKING METHOD TO SELECT THE CORRECT MODEL UNDER VIOLATION OF CERTAIN ASSUMPTIONS OF MULTIPLE LINEAR REGRESSIONS

Ali Hussein Al-Marshadi

Advances and Applications in Statistics2026https://doi.org/10.17654/0972361726014article
ABDC C
Weight
0.50

What the paper says

This paper deals with comparing some information criteria with one ranking method to select the correct model when the assumptions of the models are not fulfilling. The paper is based upon comparing some information criteria with one ranking method to order the available models to identifying a “correct” regression model as model number one for an available data set. The ranking method considered is the developed ranking method of Al-Marshadi et al. [6]. These findings are validated using the extensive simulation study. We have found that the developed ranking method is best in selecting the “correct” regression model as number one model in simulation study comparing to the information criteria. It is, therefore, suggested that the developed ranking method can be used to identifying a “correct” regression model as model number one even under violation of some assumptions of the model.

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https://doi.org/https://doi.org/10.17654/0972361726014

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@article{ali2026,
  title        = {{COMPARISON OF SOME INFORMATION CRITERIA WITH THE DEVELOPED RANKING METHOD TO SELECT THE CORRECT MODEL UNDER VIOLATION OF CERTAIN ASSUMPTIONS OF MULTIPLE LINEAR REGRESSIONS}},
  author       = {Ali Hussein Al-Marshadi},
  journal      = {Advances and Applications in Statistics},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.17654/0972361726014},
}

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COMPARISON OF SOME INFORMATION CRITERIA WITH THE DEVELOPED RANKING METHOD TO SELECT THE CORRECT MODEL UNDER VIOLATION OF CERTAIN ASSUMPTIONS OF MULTIPLE LINEAR REGRESSIONS

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.