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.
- An 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 published extensively on his research interests, which include cluster analysis, pattern recognition, random-effects modelling, and survival analysis.
Liming Xiang is Associate Professor of Statistics in the School of Physical & Mathematical Sciences at the Nanyang Technological University, Singapore. Her research interests include survival analysis, longitudinal/clustered data analysis and mixture models.
Kelvin Kai-wing Yau is Professor of Statistics in the Department of Management Sciences at the City University of Hong Kong. He has been involved in various interdisciplinary research projects, with journal publications in statistics, medical and health science journals on topics such as mixed effects models, survival analysis and statistical modelling in general.
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.
Dr Angus Ng is a Professor of Biostatistics in the School of Medicine, Griffith University. He was awarded his PhD degree in statistics from the University of Queensland in 1999. Dr Ng is an experienced researcher, with expertise in the fields of biostatistics, statistical modelling, cluster analysis, pattern recognition, machine learning, image analysis, and survival analysis. In these areas, he has more than 100 publications. The focus in the field of statistical modelling has been on the theory and applications of finite mixture models and on estimation via the EM algorithm. In his pioneering work on mixture model-based clustering of longitudinal data, he has elucidated a clear vision for the role of random-effects models to provide a sound theoretical framework for classifying correlated longitudinal data and exploring possible relationships among groups of correlated subjects.
Dr Ng was awarded six ARC grants and has been actively involved in multidisciplinary research projects, NHMRC research projects, as well as consultancy and Government contracts. He is also a researcher with the Centre for Applied Health Economics (CAHE) and is an Associate Editor of the Journal of Statistical Computation and Simulation.
Prof. Kelvin Yau is a retired professor in the department of management sciences at the City University of Hong Kong. His research interests include Generalized Linear Mixed Models, Multivariate Survival Analysis, Finite Mixture Models, Robust Estimation, Statistical Modelling and Zero-Inflated-Poisson Models.
Liming Xiang is a professor of statistics at Nanyang Technological University in Singapore. She got her PhD degree in 2002 from the City University of Hong Kong. She serves as associate editor for Statistics in Medicine, Computational Statistics & Data Analysis and Journal of Statistical Computation and Simulation.
"...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