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Adaptive Regression for Modeling Nonlinear RelationshipsAdaptive Regression Modeling of Univariate Continuous Outcomes in SAS

Adaptive Regression for Modeling Nonlinear Relationships: Adaptive Regression Modeling of... [This chapter describes how to use the genreg (for general regression) macro for adaptive regression modeling, with models for the means linear in their intercept and slope parameters, and its generated output in the special case of univariate continuous outcomes as also covered in Chap. 2. Example code and output are provided addressing analyses of death rates per 100,000 for 60 metropolitan statistical areas in terms of the nitric oxide pollution index, the sulfur dioxide pollution index, and the average annual precipitation. Issues covered include loading the data; setting the number k of folds for computing k-fold likelihood cross-validation (LCV) scores; generating standard polynomial models, fractional polynomial models, monotonic models, and zero-intercept models; incorporating log transforms and multiple primary predictors; model selection using penalized likelihood criteria (PLCs) rather than LCV; bounding primary predictors; residual analyses; and modeling variances as well as means. Practice exercises are also provided for conducting analyses similar to those presented in Chaps. 2 and 3.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Adaptive Regression for Modeling Nonlinear RelationshipsAdaptive Regression Modeling of Univariate Continuous Outcomes in SAS

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Publisher
Springer International Publishing
Copyright
© Springer International Publishing Switzerland 2016
ISBN
978-3-319-33944-3
Pages
45 –62
DOI
10.1007/978-3-319-33946-7_3
Publisher site
See Chapter on Publisher Site

Abstract

[This chapter describes how to use the genreg (for general regression) macro for adaptive regression modeling, with models for the means linear in their intercept and slope parameters, and its generated output in the special case of univariate continuous outcomes as also covered in Chap. 2. Example code and output are provided addressing analyses of death rates per 100,000 for 60 metropolitan statistical areas in terms of the nitric oxide pollution index, the sulfur dioxide pollution index, and the average annual precipitation. Issues covered include loading the data; setting the number k of folds for computing k-fold likelihood cross-validation (LCV) scores; generating standard polynomial models, fractional polynomial models, monotonic models, and zero-intercept models; incorporating log transforms and multiple primary predictors; model selection using penalized likelihood criteria (PLCs) rather than LCV; bounding primary predictors; residual analyses; and modeling variances as well as means. Practice exercises are also provided for conducting analyses similar to those presented in Chaps. 2 and 3.]

Published: Sep 21, 2016

Keywords: Metropolitan Statistical Area; Residual Analysis; Adaptive Model; Decimal Digit; Primary Predictor

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