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Selected Contributions in Data Analysis and ClassificationLocally Linear Regression and the Calibration Problem for Micro-Array Analysis

Selected Contributions in Data Analysis and Classification: Locally Linear Regression and the... [We review the concept of locally linear regression and its relationship to Diday’s Nuées Dynamiques and to tree-structured linear regression. We describe the calibration problem in microarray analysis and propose a Bayesian approach based on tree-structured linear regression. Using the proposed approach, we analyze a subset of a large data set from an Affymetrix microarray calibration experiment. In this example, a tree-structured regression model outperforms a multiple regression model. We calculated 95% Credible Intervals for a sample of the data, obtaining reasonably good results. Future research will consider and compare several other approaches to locally linear regression.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Selected Contributions in Data Analysis and ClassificationLocally Linear Regression and the Calibration Problem for Micro-Array Analysis

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References (6)

Publisher
Springer Berlin Heidelberg
Copyright
© Springer-Verlag Berlin Heidelberg 2007
ISBN
978-3-540-73558-8
Pages
549 –555
DOI
10.1007/978-3-540-73560-1_51
Publisher site
See Chapter on Publisher Site

Abstract

[We review the concept of locally linear regression and its relationship to Diday’s Nuées Dynamiques and to tree-structured linear regression. We describe the calibration problem in microarray analysis and propose a Bayesian approach based on tree-structured linear regression. Using the proposed approach, we analyze a subset of a large data set from an Affymetrix microarray calibration experiment. In this example, a tree-structured regression model outperforms a multiple regression model. We calculated 95% Credible Intervals for a sample of the data, obtaining reasonably good results. Future research will consider and compare several other approaches to locally linear regression.]

Published: Jan 1, 2007

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