engleză
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.
Features
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.
