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good predictive behavior. 

## A Appendix 

Before showing some of the properties of the MM-estimate, we set the notation for norms of vectors and matrices that we will use later: 

Given v ∈ R $^{m}$, we denote its 2-norm or Euclidean norm as: 

where v$_{i}$ represents the i th element of v . 

<img src='content_image/314689.jpg'>

Given a matrix A ∈ R r × $^{m}$, its spectral norm ‖ · ‖ $_{sp}$, is defined as follows: 

<img src='content_image/314688.jpg'>

Its 2-norm or Frobenius norm ‖ · ‖ 2 is its Euclidean norm if we think the matrix A as a vector of R $^{m.r}$, i.e. 

<img src='content_image/314687.jpg'>

where a$_{ij}$ represents to the ( i, j )th element of the matrix A and tr( · ) denotes the trace. 

Given V ∈ R m × m we denote its eigenvalues as 

<img src='content_image/314686.jpg'>

