  This paper is devoted to the offline multiple changes detection for
long-range dependence processes. The observations are supposed to satisfy a
semi-parametric long-range dependence assumption with distinct memory
parameters on each stage. A penalized local Whittle contrast is considered for
estimating all the parameters, notably the number of changes. The consistency
as well as convergence rates are obtained. Monte-Carlo experiments exhibit the
accuracy of the estimators. They also show that the estimation of the number of
breaks is improved by using a data-driven slope heuristic procedure of choice
of the penalization parameter.
