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Robust principal component analysis based on trimming around affine subspaces

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Figure

Figure 6.1: Influence function of the largest eigenvalue at P = N (0, diag(2, 1)) when α = 0 (left panel) and α = 0.01 (right panel).
Figure 6.2: Influence function of the eigenvector associated to the largest eigenvalue at P = N (0, diag(2, 1)) when α = 0 (left panel) and α = 0.01 (right panel).
Table 8.1: Finite sample efficiencies for the trimmed PCA w.r.t. the ML. Design (a) p d α n Eigenvalues Eigenvectors 5 3 .05 50 .992 .754 .677 .590 100 .979 .918 .845 .710 500 .942 .927 .900 .852 ∞ .950 .932 .922 .846 5 3 .10 50 .985 .652 .608 .502 100 .91
Figure 8.3: Simulated value of D 2 as a function of the percentage of outliers for 6 different estimators, for design (a) with n = 50, p = 5, and d = 3.
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