Description
Book SynopsisMixture 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.
Features
- An in-depth account of the most up-to-date mixture modelling techniques from auser perspective.
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Trade Review
"...The examples are rich in diagrams and tables, with explanatory text. The coding parts are less extensive. In any case, such a homogenic structure of the book definitely contributes to increased readability and understandability of quite complex topics. This is especially true in the later chapters, where more advanced methods are discussed...To conclude, this book is a definite asset for those interested in sample clustering and more specifically mixture modelling."
- Gia Jgarkava, ISCB News, July 2020
"...(This book) by Shu Kay Ng, Liming Xiang and Kelvin Kai Wing Yau connects theoretical modelling to many real-world problems. Noteworthy features of this fascinating book include in-depth up-to-date knowledge on mixture modeling, random effects, among others...The bibliography is exhaustive and complete for the sake of the readers."
- Ramalingam Shanmugam, JSCS, Aug 2020
Table of Contents
1. Introduction. 2. Mixture of Normal Distributions for Continuous Data. 3. Mixture of Gamma Distributions for Continuous Non-Normal Data. 4. Mixture of Generalized Linear Models for Count or Categorical Data. 5. Mixture Models for Survival Data. 6. Advanced Mixture Modelling with Random-Effects Components. 7. Advanced Mixture Models for Multilevel or Repeated-Measured Data. 8. Continuous Data. 9. Miscellaneous: Handling of Missing Data. 10. Miscellaneous: Cluster Analysis of "Big Data" Using Mixture Models.
