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Adaptive Regression for Modeling Nonlinear RelationshipsMultivariate Adaptive Regression Spline Modeling in SAS

Adaptive Regression for Modeling Nonlinear Relationships: Multivariate Adaptive Regression Spline... [This chapter provides a description of how to use PROC ADAPTIVEREG for generating multivariate adaptive regression splines (MARS) models for univariate continuous and dichotomous outcomes as well as how to evaluate and compare MARS models with likelihood cross-validation (LCV) scores. Comparison of MARS models to adaptive fractional polynomial models on the basis of LCV scores is also covered as well as how to adaptively transform MARS models. Example code is provided for generating models for predicting the univariate continuous outcome death rate per 100,000 in terms of available predictors as also addressed in Chaps 2, 3, 16, and 17 as well as models for predicting the univariate dichotomous outcome a high mercury level in fish over 1.0 ppm versus a lower level in terms of available predictors as also addressed in Chaps 8, 9, 16 and 17.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Adaptive Regression for Modeling Nonlinear RelationshipsMultivariate Adaptive Regression Spline Modeling in SAS

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References (2)

Publisher
Springer International Publishing
Copyright
© Springer International Publishing Switzerland 2016
ISBN
978-3-319-33944-3
Pages
339 –349
DOI
10.1007/978-3-319-33946-7_19
Publisher site
See Chapter on Publisher Site

Abstract

[This chapter provides a description of how to use PROC ADAPTIVEREG for generating multivariate adaptive regression splines (MARS) models for univariate continuous and dichotomous outcomes as well as how to evaluate and compare MARS models with likelihood cross-validation (LCV) scores. Comparison of MARS models to adaptive fractional polynomial models on the basis of LCV scores is also covered as well as how to adaptively transform MARS models. Example code is provided for generating models for predicting the univariate continuous outcome death rate per 100,000 in terms of available predictors as also addressed in Chaps 2, 3, 16, and 17 as well as models for predicting the univariate dichotomous outcome a high mercury level in fish over 1.0 ppm versus a lower level in terms of available predictors as also addressed in Chaps 8, 9, 16 and 17.]

Published: Sep 21, 2016

Keywords: Mercury Level; Largemouth Bass; Dichotomous Outcome; Multivariate Adaptive Regression Spline; Adaptive Model

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