Fixed points of the multivariate smoothing transform: the critical case

Abstract

Given a sequence T_1, T_2, ... of random d-by-d matrices with nonnegative entries, suppose there is a random vector X with nonnegative entries, such that the sum T_1 X_1 + T_2 X_2 +... has the same law as X, with X_1, X_2, ... being i.i.d.copies of X, independent of T_1, T_2, ... Then (the law of) X is called a fixed point of the multivariate smoothing transform. Similar to the well-studied one-dimensional case d=1, a function m is introduced, such that the existence of $\alpha \in (0,1]$ with $m(\alpha)=1$ and $m'(\alpha) \le 0$ guarantees the existence of nontrivial fixed points. We prove the uniqueness of fixed points in the critical case $m'(\alpha)=0$ and describe their tail behavior. This complements recent results for the non-critical multivariate case. Moreover, we introduce the multivariate analogue of the derivative martingale and prove its convergence to a non-trivial limit.