  Max-mixture processes are defined as Z = max(aX, (1 -- a)Y) with X an
asymptotic dependent (AD) process, Y an asymptotic independent (AI) process and
a $\in$ [0, 1]. So that, the mixing coefficient a may reveal the strength of
the AD part present in the max-mixture process. In this paper we focus on two
tests based on censored pairwise likelihood estimates. We compare their
performance through an extensive simulation study. Monte Carlo simulation plays
a fundamental tool for asymptotic variance calculations. We apply our tests to
daily precipitations from the East of Australia. Drawbacks and possible
developments are discussed.
