Adaptive Regression for Modeling Nonlinear Relationships

Name: Adaptive Regression for Modeling Nonlinear Relationships
Brand: Springer International Publishing AG
SKU: 9783319339443
Price: 378.0 RON
Availability: InStock

Autor: George J. Knafl, Kai Ding

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Legare: Copertă tare

Editura: Springer International Publishing AG

Disponibilitate: În depozitul extern

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378.00 lei

This book presents methods for investigating whether relationships are linear or nonlinear and for f...

Informații despre carte

Autor

George J. Knafl, Kai Ding

Limbă

engleză

Legare

Carte - Copertă tare

Publicat

2016

Pagini

372

EAN

9783319339443

ISBN

3319339443

Enbook ID

02975485

Editura

Springer International Publishing AG

Greutate

535

Dimensiuni

141 x 222 x 24

Descriere completă

This book presents methods for investigating whether relationships are linear or nonlinear and for fitting appropriate models when they are nonlinear. Data analysts will learn how to incorporate nonlinearity in one or more predictor variables in regression models for different types of outcome variables. Such nonlinear dependence is often not considered in applied research, yet nonlinear relationships are common and require adaptive regression. 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 power transformations of primary predictor variables using real-valued powers. The book also covers how to formulate and conduct such adaptive fractional polynomial modeling in the standard, logistic, and Poisson regression contexts with univariate or multivariate outcomes, and the book provides for 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 with 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 their output appear throughout the book, though the methodology can be applied to other programs.§

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