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Maximin estimation for gaussian graphical model

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Figure 1.1. Left panel: 10 values from an U (2,6), right panel: 10 values from U (−2,6), the full red square is the maximin effect and the empty is his estimation with the sampled values (the estimator will be discuss in chapter 2, in this case correspond
Figure 1.2. here we can see some proprieties of maximin effect using Theorem 1. The first panel shows that if 0 is inside the convex hull of the support of F B (in these example F B is discrete,
Figure 2.1. In this plot we can see how lasso and ridge work and their difference. Respect to the OLS estimator these methods shrink to 0 the parameters
Figure 2.4. in the left we can see the MM algorithm for the maximization of the function f , the function g 1 is equal to f in the point x 0 , maximizing g 1 we find x 1 and the function g 2 , the last one
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