The Resource Adaptive regression for modeling nonlinear relationships, George J. Knafl, Kai Ding

Adaptive regression for modeling nonlinear relationships, George J. Knafl, Kai Ding

Label
Adaptive regression for modeling nonlinear relationships
Title
Adaptive regression for modeling nonlinear relationships
Statement of responsibility
George J. Knafl, Kai Ding
Creator
Contributor
Subject
Genre
Language
eng
Summary
This 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
Member of
Cataloging source
EBLCP
http://library.link/vocab/creatorName
Knafl, George J
Dewey number
519.5/36
Index
index present
LC call number
QA278.2
Literary form
non fiction
Nature of contents
  • dictionaries
  • bibliography
http://library.link/vocab/relatedWorkOrContributorName
Ding, Kai
Series statement
Statistics for biology and health
http://library.link/vocab/subjectName
  • Regression analysis
  • Nonlinear theories
  • Medicine
  • Mathematical statistics
  • Biometry
  • MATHEMATICS
  • MATHEMATICS
  • Biometry
  • Mathematical statistics
  • Medicine
  • Nonlinear theories
  • Regression analysis
Label
Adaptive regression for modeling nonlinear relationships, George J. Knafl, Kai Ding
Link
https://ezproxy.lib.ou.edu/login?url=http://link.springer.com/10.1007/978-3-319-33946-7
Instantiates
Publication
Bibliography note
Includes bibliographical references and index
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
Introduction -- 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
Dimensions
unknown
Extent
1 online resource.
Form of item
online
Isbn
9783319339467
Media category
computer
Media MARC source
rdamedia
Media type code
c
Note
SpringerLink
Other control number
10.1007/978-3-319-33946-7
Specific material designation
remote
System control number
  • (OCoLC)959150999
  • (OCoLC)ocn959150999
Label
Adaptive regression for modeling nonlinear relationships, George J. Knafl, Kai Ding
Link
https://ezproxy.lib.ou.edu/login?url=http://link.springer.com/10.1007/978-3-319-33946-7
Publication
Bibliography note
Includes bibliographical references and index
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
Introduction -- 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
Dimensions
unknown
Extent
1 online resource.
Form of item
online
Isbn
9783319339467
Media category
computer
Media MARC source
rdamedia
Media type code
c
Note
SpringerLink
Other control number
10.1007/978-3-319-33946-7
Specific material designation
remote
System control number
  • (OCoLC)959150999
  • (OCoLC)ocn959150999

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