content_image/301120.jpg content_image/301121.jpg content_image/301122.jpg content_image/301123.jpg content_image/301125.jpg content_image/301124.jpg
and by (2.4) and (2.1) 

<img src='content_image/301120.jpg'>

and this proves the Theorem. ■ 

Before proving Theorem 2 we will give some results on matrix derivatives that will be used later. 

Let b be a vector and V be a symmetric matrix, 

if V is nonsingular, 

and 

For further details see Chapter 17 of [24]. 

<img src='content_image/301121.jpg'>

<img src='content_image/301122.jpg'>

<img src='content_image/301123.jpg'>

Let ( B , Σ ) ∈ R p × q × S $_{q}$, using vec( B $^{′}$A ) = ( A ⊗ I$_{q}$ )vec( B $^{′}$), it is easy to check that 

From (A.16) and (A.13) it follows that 

<img src='content_image/301125.jpg'>

<img src='content_image/301124.jpg'>

Proof of Theorem 2: The definition of MM-estimates can be reformulated, us- ing the function Γ ( Σ ) := Σ / | Σ | 1 $^{/q}$, in the following way: let ( ̂ B$_{n}$ , C$_{n}$ ) be any 

