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200 10$aIdentifiability and Regression Analysis of Biological Systems Models$b[electronic resource] $eStatistical and Mathematical Foundations and R Scripts /$fby Paola Lecca
205   $a1st ed. 2020.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020.
215   $a1 online resource (X, 82 p. 13 illus., 8 illus. in color.)
225 1 $aSpringerBriefs in Statistics,$x2191-544X
311   $a3-030-41254-7 
327   $a1 Complex systems and sets of data -- 2 Dynamic models -- 3 Model identifiability -- 4 Relationships between phenomena -- 5 Codes.
330   $aThis richly illustrated book presents the objectives of, and the latest techniques for, the identifiability analysis and standard and robust regression analysis of complex dynamical models. The book first provides a definition of complexity in dynamic systems by introducing readers to the concepts of system size, density of interactions, stiff dynamics, and hybrid nature of determination. In turn, it presents the mathematical foundations of and algorithmic procedures for model structural and practical identifiability analysis, multilinear and non-linear regression analysis, and best predictor selection. Although the main fields of application discussed in the book are biochemistry and systems biology, the methodologies described can also be employed in other disciplines such as physics and the environmental sciences. Readers will learn how to deal with problems such as determining the identifiability conditions, searching for an identifiable model, and conducting their own regression analysis and diagnostics without supervision. Featuring a wealth of real-world examples, exercises, and codes in R, the book addresses the needs of doctoral students and researchers in bioinformatics, bioengineering, systems biology, biophysics, biochemistry, the environmental sciences and experimental physics. Readers should be familiar with the fundamentals of probability and statistics (as provided in first-year university courses) and a basic grasp of R.
410  0$aSpringerBriefs in Statistics,$x2191-544X
606   $aStatistics 
606   $aSystems biology
606   $aBiostatistics
606   $aBiomathematics
606   $aR (Computer program language)
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615  0$aStatistics .
615  0$aSystems biology.
615  0$aBiostatistics.
615  0$aBiomathematics.
615  0$aR (Computer program language).
615 14$aStatistics for Life Sciences, Medicine, Health Sciences.
615 24$aSystems Biology.
615 24$aBiostatistics.
615 24$aStatistical Theory and Methods.
615 24$aMathematical and Computational Biology.
615 24$aStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
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906   $aBOOK
912   $a996418199703316
996   $aIdentifiability and Regression Analysis of Biological Systems Models$92251851
997   $aUNISA