Minimum average partial correlation and parallel analysis : the influence of oblique structures [r-libre/1393]

Caron, Pier-Olivier (2019). Minimum average partial correlation and parallel analysis : the influence of oblique structures. Communications in Statistics - Simulation and Computation, 48 (7), 2110-2117. https://doi.org/10.1080/03610918.2018.1433843

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Catégorie de document :	Articles de revues
Évaluation par un comité de lecture :	Oui
Étape de publication :	Publié
Résumé :	Parallel analysis (Horn 1965) and the minimum average partial correlation (MAP; Velicer 1976) have been widely spread as optimal solutions to identify the correct number of axes in principal component analysis. Previous results showed, however, that they become inefficient when variables belonging to different components strongly correlate. Simulations are used to assess their power to detect the dimensionality of data sets with oblique structures. Overall, MAP had the best performances as it was more powerful and accurate than PA when the component structure was modestly oblique. However, both stopping rules performed poorly in the presence of highly oblique factors.
Adresse de la version officielle :	http://www.tandfonline.com/eprint/FFcsWdEVcHNPB2Fx...
Déposant:	Caron, Pier-Olivier
Responsable :	Pier-Olivier Caron
Dépôt :	12 févr. 2018 18:54
Dernière modification :	16 août 2021 15:05

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