000 05627nam a22005415i 4500
001 978-3-319-33946-7
003 DE-He213
005 20181204134227.0
007 cr nn 008mamaa
008 160920s2016 gw | s |||| 0|eng d
020 _a9783319339467
_9978-3-319-33946-7
024 7 _a10.1007/978-3-319-33946-7
_2doi
040 _aISI Library, Kolkata
050 4 _aQA276-280
072 7 _aPBT
_2bicssc
072 7 _aMED090000
_2bisacsh
072 7 _aPBT
_2thema
072 7 _aMBNS
_2thema
082 0 4 _a519.5
_223
100 1 _aKnafl, George J.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aAdaptive Regression for Modeling Nonlinear Relationships
_h[electronic resource] /
_cby George J. Knafl, Kai Ding.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2016.
300 _aXXV, 372 p. 57 illus., 13 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStatistics for Biology and Health,
_x1431-8776
505 0 _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. .
520 _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.
650 0 _aStatistics.
650 0 _aMathematical statistics.
650 0 _aStatistical methods.
650 1 4 _aStatistics for Life Sciences, Medicine, Health Sciences.
_0http://scigraph.springernature.com/things/product-market-codes/S17030
650 2 4 _aStatistical Theory and Methods.
_0http://scigraph.springernature.com/things/product-market-codes/S11001
650 2 4 _aBiostatistics.
_0http://scigraph.springernature.com/things/product-market-codes/L15020
700 1 _aDing, Kai.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319339443
776 0 8 _iPrinted edition:
_z9783319339450
776 0 8 _iPrinted edition:
_z9783319816388
830 0 _aStatistics for Biology and Health,
_x1431-8776
856 4 0 _uhttps://doi.org/10.1007/978-3-319-33946-7
912 _aZDB-2-SMA
942 _cEB
950 _aMathematics and Statistics (Springer-11649)
999 _c426457
_d426457