Mixture Modelling for Medical and Health Sciences provides a direct connection between theoretical developments in mixture modelling and their applications in real world problems. The book describes the development of the most important concepts through comprehensive analyses of real and practical examples taken from real-life research problems in medical and health sciences. This approach represents balance between "theory" and "practice", stimulating readers and enhancing their capacity to apply mixture models in data analysis. Full of reproducible examples using software code and publicly-available data, the book is suitable for graduate-level students, researchers, and practitioners who have a basic grounding in statistics and would like to explore the use of mixture models to analyse their experiments and research data.FeaturesAn in-depth account of the most up-to-date mixture modelling techniques from auser perspective.Extensive real-life examples – from typical daily problems to complex data modelling. Emphasis on the use of a wide variety of component densities for statistical modelling.Coverage of the latest random-effects models in modelling complex correlated data.An accompanying website to provide supplementary materials, including software and detailed programming code, and links to available data sources. Provision of R and Fortran code for readers who want to do analysis of their own data using mixture models. Shu-Kay Angus Ng is Professor of Biostatistics in the School of Medicine at the Griffith University, Australia. Dr Ng has pub

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