LEADER 03915nam  22006855  450 
001 9910481964703321
005 20250318115243.0
010   $a9783030412555
010   $a3030412555
024 7 $a10.1007/978-3-030-41255-5
035   $a(CKB)5300000000003484
035   $a(DE-He213)978-3-030-41255-5
035   $a(MiAaPQ)EBC6129282
035   $a(PPN)243226470
035   $a(EXLCZ)995300000000003484
100   $a20200305d2020      u|         0
101 0 $aeng
135   $aurnn#008mamaa
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aIdentifiability and Regression Analysis of Biological Systems Models $eStatistical and Mathematical Foundations and R Scripts /$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 $aSpringerBriefs in Statistics,$x2191-5458
311 08$a9783030412548 
311 08$a3030412547 
327   $a1 Complex systems and sets of data -- 2 Dynamic models -- 3 Model identifiability -- 4 Relationships between phenomena -- 5 Codes.
330   $aThis richly illustrated book presents the objectives of, and the latest techniques for, the identifiability analysis and standard and robust regression analysis of complex dynamical models. The book first provides a definition of complexity in dynamic systems by introducing readers to the concepts of system size, density of interactions, stiff dynamics, and hybrid nature of determination. In turn, it presents the mathematical foundations of and algorithmic procedures for model structural and practical identifiability analysis, multilinear and non-linear regression analysis, and best predictor selection. Although the main fields of application discussed in the book are biochemistry and systems biology, the methodologies described can also be employed in other disciplines such as physics and the environmental sciences. Readers will learn how to deal with problems such as determining the identifiability conditions, searching for an identifiable model, and conducting theirown regression analysis and diagnostics without supervision. Featuring a wealth of real-world examples, exercises, and codes in R, the book addresses the needs of doctoral students and researchers in bioinformatics, bioengineering, systems biology, biophysics, biochemistry, the environmental sciences and experimental physics. Readers should be familiar with the fundamentals of probability and statistics (as provided in first-year university courses) and a basic grasp of R.
410  0$aSpringerBriefs in Statistics,$x2191-5458
606   $aBiometry
606   $aBioinformatics
606   $aStatistics
606   $aBiomathematics
606   $aStatistics
606   $aBiostatistics
606   $aComputational and Systems Biology
606   $aStatistical Theory and Methods
606   $aMathematical and Computational Biology
606   $aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences
615  0$aBiometry.
615  0$aBioinformatics.
615  0$aStatistics.
615  0$aBiomathematics.
615  0$aStatistics.
615 14$aBiostatistics.
615 24$aComputational and Systems Biology.
615 24$aStatistical Theory and Methods.
615 24$aMathematical and Computational Biology.
615 24$aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
676   $a572.0727
700   $aLecca$b Paola$4aut$4http://id.loc.gov/vocabulary/relators/aut$0985245
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910481964703321
996   $aIdentifiability and Regression Analysis of Biological Systems Models$92251851
997   $aUNINA