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Identifiability and regression analy...

Lecca, Paola.

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Identifiability and regression analysis of biological systems models = statistical and mathematical foundations and R scripts /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Identifiability and regression analysis of biological systems models/ by Paola Lecca.
Reminder of title:	statistical and mathematical foundations and R scripts /
Author:	Lecca, Paola.
Published:	Cham :Springer International Publishing : : 2020.,
Description:	x, 82 p. :ill., digital ;24 cm.
[NT 15003449]:	1 Complex systems and sets of data -- 2 Dynamic models -- 3 Model identifiability -- 4 Relationships between phenomena -- 5 Codes.
Contained By:	Springer eBooks
Subject:	Regression analysis. -
Online resource:	https://doi.org/10.1007/978-3-030-41255-5
ISBN:	9783030412555

Identifiability and regression analysis of biological systems models = statistical and mathematical foundations and R scripts /
Lecca, Paola.

Identifiability and regression analysis of biological systems modelsstatistical and mathematical foundations and R scripts /[electronic resource] :by Paola Lecca. - Cham :Springer International Publishing :2020. - x, 82 p. :ill., digital ;24 cm. - SpringerBriefs in statistics,2191-544X. - SpringerBriefs in statistics..

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 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.

ISBN: 9783030412555

Standard No.: 10.1007/978-3-030-41255-5doiSubjects--Topical Terms:

529831
Regression analysis.

LC Class. No.: QA278.2 / .L433 2020

Dewey Class. No.: 519.536

Identifiability and regression analysis of biological systems models = statistical and mathematical foundations and R scripts /
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505 0 $a 1 Complex systems and sets of data -- 2 Dynamic models -- 3 Model identifiability -- 4 Relationships between phenomena -- 5 Codes.
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