About this book
This richly illustrated book presents the objectives of, and the latest techniques for, the identifiability analysis and standard and robust regression analysis of complex dynamical models. The book first provides a definition of complexity in dynamic systems by introducing readers to the concepts of system size, density of interactions, stiff dynamics, and hybrid nature of determination. In turn, it presents the mathematical foundations of and algorithmic procedures for model structural and practical identifiability analysis, multilinear and non-linear regression analysis, and best predictor selection.
Although the main fields of application discussed in the book are biochemistry and systems biology, the methodologies described can also be employed in other disciplines such as physics and the environmental sciences. Readers will learn how to deal with problems such as determining the identifiability conditions, searching for an identifiable model, and conducting their own regression analysis and diagnostics without supervision.
Featuring a wealth of real-world examples, exercises, and codes in R, the book addresses the needs of doctoral students and researchers in bioinformatics, bioengineering, systems biology, biophysics, biochemistry, the environmental sciences and experimental physics. Readers should be familiar with the fundamentals of probability and statistics (as provided in first-year university courses) and a basic grasp of R.
- Book Title Identifiability and Regression Analysis of Biological Systems Models
- Book Subtitle Statistical and Mathematical Foundations and R Scripts
- Series Title SpringerBriefs in Statistics
- Series Abbreviated Title SpringerBriefs in Statistics
- DOI https://doi.org/10.1007/978-3-030-41255-5
- Copyright Information The Author(s), under exclusive license to Springer Nature Switzerland AG 2020
- Publisher Name Springer, Cham
- eBook Packages Mathematics and Statistics Mathematics and Statistics (R0)
- Softcover ISBN 978-3-030-41254-8
- eBook ISBN 978-3-030-41255-5
- Series ISSN 2191-544X
- Series E-ISSN 2191-5458
- Edition Number 1
- Number of Pages X, 82
- Number of Illustrations 5 b/w illustrations, 8 illustrations in colour
Statistics for Life Sciences, Medicine, Health Sciences
Statistical Theory and Methods
Mathematical and Computational Biology
Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences
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