Mixture Models: Parametric, Semiparametric, and New Directions
Safaa K. Kadhem · Journal of the Royal Statistical Society Series A (Statistics in Society) · 2024
This book targets those interested in behaviour of complex or heterogeneous data and focuses in particular on analysing and modelling it with a variety of mixture models. In addition to the applied statisticians, it may be valuable in particular for others interested in the linear regression models and causal inference, where the complexity handling in those models when study the mixture data as a case. The amount of examples to data was fine and covered several fields such astronomy, biology, genomics, economics, finance, medicine, engineering, and sociology were applied which can be more interesting for the statisticians and postgraduate students and others in particular would be familiar with. The bibliography provides ample references closely related to the related to the book’s topics. I can say that this book can occupy a special place among what I have read of fantastic textbooks (this my opinion and I do not underestimate other sources) such Finite Mixture and Markov Switching Models by Frühwirth-Schnatter (2006) as well Finite Mixture Models by McLachlan et al. (2019) in its latest edition.