  Mixture models have received a great deal of attention in statistics due to
the wide range of applications found in recent years. This paper discusses a
finite mixture model of Birnbaum- Saunders distributions with G components, as
an important supplement of the work developed by Balakrishnan et al. (2011),
who only considered two components. Our proposal enables the modeling of proper
multimodal scenarios with greater flexibility, where the identifiability of the
model with G components is proven and an EM-algorithm for the maximum
likelihood (ML) estimation of the mixture parameters is developed, in which the
k-bumps algorithm is used as an initialization strategy in the EM algorithm.
The performance of the k-bumps algorithm as an initialization tool is evaluated
through simulation experiments. Moreover, the empirical information matrix is
derived analytically to account for standard error, and bootstrap procedures
for testing hypotheses about the number of components in the mixture are
implemented. Finally, we perform simulation studies and analyze two real
datasets to illustrate the usefulness of the proposed method.
