Finite mixture models are routinely applied to time course microarray data.
Due to the complexity and size of this type of data the choice of good starting values plays
an important role. So far initialization strategies have only been investigated for data
from a mixture of multivariate normal distributions. In this work several initialization
procedures are evaluated for mixtures of regression models with and without random
effects in an extensive simulation study on different artificial datasets. Finally these
procedures are also applied to a real dataset from E. coli