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Mixture Modelling for Medical and Health Sciences (Paperback)
Loot Price: R1,482
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Mixture Modelling for Medical and Health Sciences (Paperback)
Series: Chapman & Hall/CRC Biostatistics Series
Expected to ship within 12 - 17 working days
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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 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.
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