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Adaptive Regression for Modeling Nonlinear RelationshipsAdaptive Transformation of Positive Valued Continuous Outcomes

Adaptive Regression for Modeling Nonlinear Relationships: Adaptive Transformation of Positive... [This chapter presents analyses of several data sets with positive valued univariate or multivariate continuous outcomes addressing the need for power transformation of those outcomes along with power transformation of predictors for those outcomes. The outcome variables include those analyzed in Chaps. 2–5 as well as a new data set on plasma levels of beta-carotene in humans in terms of their fiber intake and vitamin usage. The chapter also provides a formulation for power-adjusted likelihood cross-validation (LCV) scores that can be maximized to choose a real valued power for transforming an outcome.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Adaptive Regression for Modeling Nonlinear RelationshipsAdaptive Transformation of Positive Valued Continuous Outcomes

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References (4)

Publisher
Springer International Publishing
Copyright
© Springer International Publishing Switzerland 2016
ISBN
978-3-319-33944-3
Pages
133 –147
DOI
10.1007/978-3-319-33946-7_6
Publisher site
See Chapter on Publisher Site

Abstract

[This chapter presents analyses of several data sets with positive valued univariate or multivariate continuous outcomes addressing the need for power transformation of those outcomes along with power transformation of predictors for those outcomes. The outcome variables include those analyzed in Chaps. 2–5 as well as a new data set on plasma levels of beta-carotene in humans in terms of their fiber intake and vitamin usage. The chapter also provides a formulation for power-adjusted likelihood cross-validation (LCV) scores that can be maximized to choose a real valued power for transforming an outcome.]

Published: Sep 21, 2016

Keywords: Univariate Outcome; Generalize Estimate Equation; Constant Variance; Standardize Residual; Normal Density Function

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