Mixture Modelling for Medical and Health Sciences (2019)

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    Finite mixture models are powerful statistical tools with a sound theoretical basis for modelling the distribution of a heterogeneous population as well as providing a clustering of sample data to extract useful information on the group structure of the underlying population. Over the past few decades, there has been an ever-increasing use of finite mixture models to provide a model-based approach to the analysis of data arising in a wide variety of scientific fields, including bioinformatics, biometrics/biostatistics, data science, economics, health, image analysis, medicine, and psycho-oncology, among many others.
    Mixture models with normal component density functions are the most widely used mixture models, given its computational tractability. There are, however, many significant advancements in extending (normal) mixture models to address realworld problems that demand highly novel methods due to new data sources, applications, and study designs for their analysis and understanding. This book presents some of these advanced mixture models developed for tackling problems that present significant methodological challenges to standard mixture modelling approaches, especially in the fields of medical and health sciences. A key aspect of these advanced approaches is the use of random-effects models to account for complex correlation structures among observations, providing a flexible modelling approach for data that are not independent. Indeed, many real-life data have been collected in settings where observations are highly correlated; these include multilevel data, longitudinal data, multivariate dependent data, multiple-source data, and “Big Data”.

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