LEADER 03646nam 2200553 450 001 9910830754003321 005 20221205231356.0 010 $a1-119-01044-6 010 $a1-119-01045-4 010 $a1-119-01046-2 035 $a(CKB)4330000000008010 035 $a(MiAaPQ)EBC5426726 035 $a(MiAaPQ)EBC6992836 035 $a(Au-PeEL)EBL6992836 035 $a(CaSebORM)9781118738061 035 $a(OCoLC)1022980304 035 $a(EXLCZ)994330000000008010 100 $a20221205d2019 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aRobust nonlinear regression $ewith applications using R /$fHossein Riazoshams, Habshah Midi and Gebrenegus Ghilagaber 205 $a1st edition 210 1$aHoboken, NJ :$cJohn Wiley & Sons, Inc.,$d[2019] 210 4$dİ2019 215 $a1 online resource (261 pages) 311 $a1-118-73806-3 320 $aIncludes bibliographical references and index. 327 $aRobust statistics -- Nonlinear models -- Robust estimators in nonlinear regression -- Heteroscedastic variance -- Authocorrelated errors -- Outlier detection in nonlinear regression -- Optimization -- Nlr package -- Robust nonlinear regression in R. 330 $aThe first book to discuss robust aspects of nonlinear regression?with applications using R software Robust Nonlinear Regression: with Applications using R covers a variety of theories and applications of nonlinear robust regression. It discusses both parts of the classic and robust aspects of nonlinear regression and focuses on outlier effects. It develops new methods in robust nonlinear regression and implements a set of objects and functions in S-language under SPLUS and R software. The software covers a wide range of robust nonlinear fitting and inferences, and is designed to provide facilities for computer users to define their own nonlinear models as an object, and fit models using classic and robust methods as well as detect outliers. The implemented objects and functions can be applied by practitioners as well as researchers. The book offers comprehensive coverage of the subject in 9 chapters: Theories of Nonlinear Regression and Inference; Introduction to R; Optimization; Theories of Robust Nonlinear Methods; Robust and Classical Nonlinear Regression with Autocorrelated and Heteroscedastic errors; Outlier Detection; R Packages in Nonlinear Regression; A New R Package in Robust Nonlinear Regression; and Object Sets. The first comprehensive coverage of this field covers a variety of both theoretical and applied topics surrounding robust nonlinear regression Addresses some commonly mishandled aspects of modeling R packages for both classical and robust nonlinear regression are presented in detail in the book and on an accompanying website Robust Nonlinear Regression: with Applications using R is an ideal text for statisticians, biostatisticians, and statistical consultants, as well as advanced level students of statistics. 606 $aNonlinear theories 606 $aR (Computer program language) 606 $aRegression analysis 615 0$aNonlinear theories. 615 0$aR (Computer program language) 615 0$aRegression analysis. 676 $a519.502855133 700 $aRiazoshams$b Hossein$f1971-$01689876 702 $aMidi$b Habshah 702 $aGhilagaber$b Gebrenegus 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910830754003321 996 $aRobust nonlinear regression$94065251 997 $aUNINA