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Bivariate non‐parametric regression models: simulations and applications

Bivariate non‐parametric regression models: simulations and applications This paper presents a practical procedure for performing non‐parametric bivariate regression analysis. The procedure applies the Nadaraya–Watson local linear kernel estimator with associated bootstrap variability bands whenever the pseudo‐likelihood ratio test rejects the linear regression model hypothesis. Two case studies and simulations are used to demonstrate the proposed technique. Calculations have been performed using the shareware R software. Copyright © 2004 John Wiley & Sons, Ltd. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Applied Stochastic Models in Business and Industry Wiley

Bivariate non‐parametric regression models: simulations and applications

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References (5)

Publisher
Wiley
Copyright
Copyright © 2004 John Wiley & Sons, Ltd.
ISSN
1524-1904
eISSN
1526-4025
DOI
10.1002/asmb.527
Publisher site
See Article on Publisher Site

Abstract

This paper presents a practical procedure for performing non‐parametric bivariate regression analysis. The procedure applies the Nadaraya–Watson local linear kernel estimator with associated bootstrap variability bands whenever the pseudo‐likelihood ratio test rejects the linear regression model hypothesis. Two case studies and simulations are used to demonstrate the proposed technique. Calculations have been performed using the shareware R software. Copyright © 2004 John Wiley & Sons, Ltd.

Journal

Applied Stochastic Models in Business and IndustryWiley

Published: Jul 1, 2004

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