The role played by the composite analogue of the log likelihood ratio in hypothesis testing
and in setting confidence regions is not as prominent as it is in the canonical likelihood set-
ting, since its asymptotic distribution depends on the unknown parameter. Approximate
pivots based on the composite log likelihood ratio can be derived by using asymptotic
arguments. However, the actual distribution of such pivots may differ considerably from
the asymptotic reference, leading to tests and confidence regions whose levels are distant
from the nominal ones. The use of bootstrap rather than asymptotic distributions in the
composite likelihood framework is explored. Prepivoted tests and confidence sets based
on a suitable statistic turn out to be accurate and computationally appealing inferential
tools.
