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Article Dans Une Revue Journal of Computational and Graphical Statistics Année : 2016

Confidence Areas for Fixed-Effects PCA

Résumé

PCA is often used to visualize data when the rows and the columns are both of interest. In such a setting there is a lack of inferential methods on the PCA output. We study the asymptotic variance of a fixed-effects model for PCA, and propose several approaches to assessing the variability of PCA estimates: a method based on a parametric bootstrap, a new cell-wise jackknife, as well as a computationally cheaper approximation to the jackknife. We visualize the confidence regions by Procrustes rotation. Using a simulation study, we compare the proposed methods and highlight the strengths and drawbacks of each method as we vary the number of rows, the number of columns, and the strength of the relationships between variables.

Dates et versions

hal-01059057 , version 1 (29-08-2014)

Identifiants

Citer

Julie Josse, Stefan Wager, François Husson. Confidence Areas for Fixed-Effects PCA. Journal of Computational and Graphical Statistics, 2016, 25 (1), pp.28-48. ⟨10.1080/10618600.2014.950871⟩. ⟨hal-01059057⟩
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