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Mischmodellierung für Medizin- und Gesundheitswissenschaften, Hardcover von Ng, Shu Kay;...-
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eBay-Artikelnr.:386937006693
Artikelmerkmale
- Artikelzustand
- Book Title
- Mixture Modelling for Medical and Health Sciences
- ISBN
- 9781482236750
- Subject Area
- Mathematics, Science, Medical
- Publication Name
- Mixture Modelling for Medical and Health Sciences
- Publisher
- CRC Press LLC
- Item Length
- 9.6 in
- Subject
- Probability & Statistics / General, General, Life Sciences / Biology
- Publication Year
- 2019
- Series
- Chapman and Hall/Crc Biostatistics Ser.
- Type
- Textbook
- Format
- Hardcover
- Language
- English
- Item Height
- 0.9 in
- Item Width
- 6.4 in
- Number of Pages
- 302 Pages
Über dieses Produkt
Product Identifiers
Publisher
CRC Press LLC
ISBN-10
1482236753
ISBN-13
9781482236750
eBay Product ID (ePID)
11038645981
Product Key Features
Number of Pages
302 Pages
Publication Name
Mixture Modelling for Medical and Health Sciences
Language
English
Subject
Probability & Statistics / General, General, Life Sciences / Biology
Publication Year
2019
Type
Textbook
Subject Area
Mathematics, Science, Medical
Series
Chapman and Hall/Crc Biostatistics Ser.
Format
Hardcover
Dimensions
Item Height
0.9 in
Item Length
9.6 in
Item Width
6.4 in
Additional Product Features
Intended Audience
College Audience
LCCN
2018-061440
Dewey Edition
23
Illustrated
Yes
Dewey Decimal
610.15195
Synopsis
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., 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 journalpublications in statistics, medical and health science journals on topics such as mixed effects models, survival analysis and statistical modelling in general.
LC Classification Number
R853.M3N4 2019
Artikelbeschreibung des Verkäufers
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