LEADER 05618nam 22005655 450 001 9910254079203321 005 20220408164949.0 010 $a3-319-33946-X 024 7 $a10.1007/978-3-319-33946-7 035 $a(CKB)3710000000866389 035 $a(DE-He213)978-3-319-33946-7 035 $a(MiAaPQ)EBC4694135 035 $a(PPN)195511670 035 $a(EXLCZ)993710000000866389 100 $a20160920d2016 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAdaptive regression for modeling nonlinear relationships /$fby George J. Knafl, Kai Ding 205 $a1st ed. 2016. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2016. 215 $a1 online resource (XXV, 372 p. 57 illus., 13 illus. in color.) 225 1 $aStatistics for Biology and Health,$x1431-8776 311 $a3-319-33944-3 320 $aIncludes bibliographical references and index. 327 $aIntroduction -- Adaptive Regression Modeling of Univariate Continuous Outcomes -- Adaptive Regression Modeling of Univariate Continuous Outcomes in SAS -- Adaptive Regression Modeling of Multivariate Continuous Outcomes -- Adaptive Regression Modeling of Multivariate Continuous Outcomes in SAS -- Adaptive Transformation of Positive Valued Continuous Outcomes -- Adaptive Logistic Regression Modeling of Univariate Dichotomous and Polytomous Outcomes -- Adaptive Logistic Regression Modeling of Univariate Dichotomous and Polytomous Outcomes in SAS -- Adaptive Logistic Regression Modeling of Multivariate Dichotomous and Polytomous Outcomes -- Adaptive Logistic Regression Modeling of Multivariate Dichotomous and Polytomous Outcomes in SAS -- Adaptive Poisson Regression Modeling of Univariate Count Outcomes -- Adaptive Poisson Regression Modeling of Univariate Count Outcomes in SAS -- Adaptive Poisson Regression Modeling of Multivariate Count Outcomes -- Adaptive Poisson Regression Modeling of Multivariate Count Outcomes in SAS -- Generalized Additive Modeling -- Generalized Additive Modeling in SAS -- Multivariate Adaptive Regression Spline Modeling -- Multivariate Adaptive Regression Spline Modeling in SAS -- Adaptive Regression Modeling Formulation. . 330 $aThis book presents methods for investigating whether relationships are linear or nonlinear and for adaptively fitting appropriate models when they are nonlinear. Data analysts will learn how to incorporate nonlinearity in one or more predictor variables into regression models for different types of outcome variables. Such nonlinear dependence is often not considered in applied research, yet nonlinear relationships are common and so need to be addressed. A standard linear analysis can produce misleading conclusions, while a nonlinear analysis can provide novel insights into data, not otherwise possible. A variety of examples of the benefits of modeling nonlinear relationships are presented throughout the book. Methods are covered using what are called fractional polynomials based on real-valued power transformations of primary predictor variables combined with model selection based on likelihood cross-validation. The book covers how to formulate and conduct such adaptive fractional polynomial modeling in the standard, logistic, and Poisson regression contexts with continuous, discrete, and counts outcomes, respectively, either univariate or multivariate. The book also provides a comparison of adaptive modeling to generalized additive modeling (GAM) and multiple adaptive regression splines (MARS) for univariate outcomes. The authors have created customized SAS macros for use in conducting adaptive regression modeling. These macros and code for conducting the analyses discussed in the book are available through the first author's website and online via the book?s Springer website. Detailed descriptions of how to use these macros and interpret their output appear throughout the book. These methods can be implemented using other programs. Provides insight into modeling of nonlinear relationships and also justifications for when to use them, thereby providing novel insights about relationships Addresses not only adaptive generation of additive models but also of models based on nonlinear interactions Discusses adaptive modeling of variances/dispersions as well as of means Highlights both univariate and multivariate outcomes, rather than solely univariate outcomes. 410 0$aStatistics for Biology and Health,$x1431-8776 606 $aStatistics  606 $aBiostatistics 606 $aStatistics for Life Sciences, Medicine, Health Sciences$3https://scigraph.springernature.com/ontologies/product-market-codes/S17030 606 $aStatistical Theory and Methods$3https://scigraph.springernature.com/ontologies/product-market-codes/S11001 606 $aBiostatistics$3https://scigraph.springernature.com/ontologies/product-market-codes/L15020 615 0$aStatistics . 615 0$aBiostatistics. 615 14$aStatistics for Life Sciences, Medicine, Health Sciences. 615 24$aStatistical Theory and Methods. 615 24$aBiostatistics. 676 $a519.536 700 $aKnafl$b George J$4aut$4http://id.loc.gov/vocabulary/relators/aut$0755792 702 $aDing$b Kai$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910254079203321 996 $aAdaptive Regression for Modeling Nonlinear Relationships$92047106 997 $aUNINA