  Recursive max-linear vectors model causal dependence between its components
by expressing each node variable as a max-linear function of its parental nodes
in a directed acyclic graph and some exogenous innovation. Motivated by extreme
value theory, innovations are assumed to have regularly varying distribution
tails. We propose a scaling technique in order to determine a causal order of
the node variables. All dependence parameters are then estimated from the
estimated scalings. Furthermore, we prove asymptotic normality of the estimated
scalings and dependence parameters based on asymptotic normality of the
empirical spectral measure. Finally, we apply our structure learning and
estimation algorithm to financial data and food dietary interview data.
