Identifiability and Regression Analysis of Biological Systems Models : Statistical and Mathematical Foundations and R Scripts. 1st ed. 2020

種類:

電子ブック

責任表示:

by Paola Lecca

出版情報:

Cham : Springer International Publishing : Imprint: Springer, 2020

著者名:

• Lecca, Paola.
• SpringerLink (Online service)

シリーズ名:

SpringerBriefs in Statistics ;

ISBN:

9783030412555 [3030412555]    

注記:

1 Complex systems and sets of data -- 2 Dynamic models -- 3 Model identifiability -- 4 Relationships between phenomena -- 5 Codes.
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

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