Summary: In this chapter, we describe developments in finite-mixture models for structured data, with a particular focus on clustered, multilevel longitudinal and multivariate data. Literature on finite mixtures in generalized linear models is now quite extensive and entails application areas such as marketing [M. Wedel and W. S. DeSarbo, J. Classif. 12, No. 1, 21–55 (1995; Zbl 0825.62611)], biostatistics [P. Wang et al., Biometrics 52, No. 2, 381–400 (1996; Zbl 0875.62407)], econometrics [P. Deb and P. K. Trivedi, J. Appl. Econometrics 12, 313–336 (1997)], machine-learning [R. A. Jacobs et al.,“Adaptive mixtures of local experts”, J. Neural Comput. 3, No. 1, 79–87 (1991; doi:10.1162/neco.1991.3.1.79)], just to mention a few. After a brief introduction, we will discuss some examples of the use of finite mixtures in heterogeneous generalized linear models, with a particular emphasis on model definition. We will also provide a brief review of available software and some suggestions on potential research areas.
For the entire collection see [Zbl 1331.68001].